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FOR EACH PAPER. Summary: Problem paper is trying to solve, key ideas/insights, mechanism, implementation. You will include key results and...
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1. Summary: Problem paper is trying to solve, key ideas/insights, mechanism, implementation. You will include key results and implementations.
2. Strenghts: Most important ones, does it solve the problem well?
Coming Challenges in Microarchitecture and
RONNY RONEN, SENIOR MEMBER, IEEE, AVI MENDELSON, MEMBER, IEEE, KONRAD LAI,
SHIH-LIEN LU, MEMBER, IEEE, FRED POLLACK, AND JOHN P. SHEN, FELLOW, IEEE
In the past several decades, the world of computers and
especially that of microprocessors has witnessed phenomenal
advances. Computers have exhibited ever-increasing performance
and decreasing costs, making them more affordable and, in turn,
accelerating additional software and hardware development
that fueled this process even more. The technology that enabled
this exponential growth is a combination of advancements in
process technology, microarchitecture, architecture, and design
and development tools. While the pace of this progress has been
quite impressive over the last two decades, it has become harder
and harder to keep up this pace. New process technology requires
more expensive megafabs and new performance levels require
larger die, higher power consumption, and enormous design and
validation effort. Furthermore, as CMOS technology continues
to advance, microprocessor design is exposed to a new set of
challenges. In the near future, microarchitecture has to consider
and explicitly manage the limits of semiconductor technology, such
as wire delays, power dissipation, and soft errors. In this paper,
we describe the role of microarchitecture in the computer world,
present the challenges ahead of us, and highlight areas where
microarchitecture can help address these challenges.
Keywords—Design tradeoffs, microarchitecture, microarchitecture
trends, microprocessor, performance improvements, power issues,
Microprocessors have gone through significant changes
during the last three decades; however, the basic computational
model has not changed much. A program consists of
instructions and data. The instructions are encoded in a specific
instruction set architecture (ISA). The computational
Manuscript received January 1, 2000; revised October 1, 2000.
R. Ronen and A. Mendelson are with the Microprocessor Research Laboratories,
Intel Corporation, Haifa 31015, Israel.
K. Lai and S.-L. Lu are with the Microprocessor Research Laboratories,
Intel Corporation, Hillsboro, OR 97124 USA.
F. Pollack and J. P. Shen are with the Microprocessor Research Laboratories,
Intel Corporation, Santa Clara, CA 95052 USA
Publisher Item Identifier S 0018-9219(01)02069-2.
model is still a single instruction stream, sequential execution
model, operating on the architecture states (memory and
registers). It is the job of the microarchitecture, the logic, and
the circuits to carry out this instruction stream in the "best"
way. "Best" depends on intended usage—servers, desktop,
and mobile—usually categorized as market segments. For
example, servers are designed to achieve the highest performance
possible while mobile systems are optimized for best
performance for a given power. Each market segment has different
features and constraints.
A. Fundamental Attributes
The key metrics for characterizing a microprocessor include:
performance, power, cost (die area), and complexity.
Performance is measured in terms of the time it takes
to complete a given task. Performance depends on many
parameters such as the microprocessor itself, the specific
workload, system configuration, compiler optimizations,
operating systems, and more. A concise characterization of
microprocessor performance was formulated by a number
of researchers in the 1980s; it has come to be known as the
"iron law" of central processing unit performance and is
Performance Execution Time
IPC Frequency Instruction Count
where is the average number of instructions completed
per cycle, is the number of clock cycles per
second, and is the total number of
instructions executed. Performance can be improved by
increasing IPC and/or frequency or by decreasing instruction
count. In practice, IPC varies depending on the environment—
the application, the system configuration, and more.
Instruction count depends on the ISA and the compiler
used. For a given executable program, where the instruction
0018-9219/01$10.00 © 2001 IEEE
PROCEEDINGS OF THE IEEE, VOL. 89, NO. 3, MARCH 2001 325
stream is invariant, the relative performance depends only on
IPC Frequency. Performance here is measured in million
instructions per second (MIPS).
Commonly used benchmark suites have been defined to
quantify performance. Different benchmarks target different
market segments, such as SPEC  and SysMark . A
benchmark suite consists of several applications. The time
it takes to complete this suite on a certain system reflects the
Power is energy consumption per unit time, in watts.
Higher performance requires more power. However, power
is constrained due to the following.
• Power density and Thermal: The power dissipated by
the chip per unit area is measured in watts/cm . Increases
in power density causes heat to generate. In
order to keep transistors within their operating temperature
range, the heat generated has to be dissipated
from the source in a cost-effective manner. Power density
may soon limit performance growth due to thermal
• Power Delivery: Power must be delivered to a very
large scale integration (VLSI) component at a prescribed
voltage and with sufficient amperage for the
component to run. Very precise voltage regulator/transformer
controls current supplies that can vary within
nanoseconds. As the current increases, the cost and
complexity of these voltage regulators/transformers
increase as well.
• Battery Life: Batteries are designed to support a certain
watts hours. The higher the power, the shorter the
time that a battery can operate.
Until recently, power efficiency was a concern only in battery
powered systems like notebooks and cell phones. Recently,
increased microprocessor complexity and frequency
have caused power consumption to grow to the level where
power has become a first-order issue. Today, each market
segment has its own power requirements and limits, making
power limitation a factor in any new microarchitecture. Maximum
power consumption is increased with the microprocessor
operating voltage ( ) and frequency (Frequency) as
where is the effective load capacitance of all devices and
wires on the microprocessor.Within some voltage range, frequency
may go up with supply voltage (
). This is a good way to gain performance,
but power is also increased (proportional to ). Another
important power related metric is the energy efficiency. Energy
efficiency is reflected by the performance/power ratio
and measured in MIPS/watt.
Cost is primarily determined by the physical size of the
manufactured silicon die. Larger area means higher (even
more than linear) manufacturing cost. Bigger die area usually
implies higher power consumption and may potentially
imply lower frequency due to longer wires. Manufacturing
yield also has direct impact on the cost of each microprocessor.
Complexity reflects the effort required to design, validate,
and manufacture a microprocessor. Complexity is affected by
the number of devices on the silicon die and the level of aggressiveness
in the performance, power and die area targets.
Complexity is discussed only implicitly in this paper.
B. Enabling Technologies
The microprocessor revolution owes it phenomenal
growth to a combination of enabling technologies: process
technology, circuit and logic techniques, microarchitecture,
architecture (ISA), and compilers.
Process technology is the fuel that has moved the entire
VLSI industry and the key to its growth. A new process generation
is released every two to three years. A process generation
is usually identified by the length of a metal-oxidesemidconductor
gate, measured in micrometers (10 m, denoted
as m). The most advanced process technology today
(year 2000) is 0.18 m .
Every new process generation brings significant improvements
in all relevant vectors. Ideally, process technology
scales by a factor of 0.7 all physical dimensions of devices
(transistors) and wires (interconnects) including those vertical
to the surface and all voltages pertaining to the devices
. With such scaling, typical improvement figures are the
• 1.4-1.5 times faster transistors;
• two times smaller transistors;
• 1.35 times lower operating voltage;
• three times lower switching power.
Theoretically, with the above figures, one would expect potential
improvements such as the following.
• Ideal Shrink: Use the same number of transistors to
gain 1.5 times performance, two times smaller die, and
two times less power.
• Ideal New Generation: Use two times the number of
transistors to gain three times performance with no increase
in die size and power.
In both ideal scenarios, there is three times gain in MIPS/watt
and no change in power density (watts/cm ).
In practice, it takes more than just process technology
to achieve such performance improvements and usually
at much higher costs. However, process technology is the
single most important technology that drives the microprocessor
industry. Growing 1000 times in frequency (from
1 MHz to 1 GHz) and integration (from 10k to 10M
devices) in 25 years was not possible without process
Innovative circuit implementations can provide better performance
or lower power. New logic families provide new
methods to realize logic functions more effectively.
Microarchitecture attempts to increase both IPC and
frequency. A simple frequency boost applied to an existing
microarchitecture can potentially reduce IPC and thus
does not achieve the expected performance increase. For
326 PROCEEDINGS OF THE IEEE, VOL. 89, NO. 3, MARCH 2001
Fig. 1. Impact of different pipeline stalls on the execution flow.
example, memory accesses latency does not scale with microprocessor
frequency. Microarchitecture techniques such
as caches, branch prediction, and out-of-order execution can
increase IPC. Other microarchitecture ideas, most notably
pipelining, help to increase frequency beyond the increase
provided by process technology.
Modern architecture (ISA) and good optimizing compilers
can reduce the number of dynamic instructions executed
for a given program. Furthermore, given knowledge of
the underlying microarchitecture, compilers can produce optimized
code that lead to higher IPC.
This paper deals with the challenges facing architecture
and microarchitecture aspects of microprocessor design. A
brief tutorial/background on traditional microarchitecture is
given in Section II, focusing on frequency and IPC tradeoffs.
Section III describes the past and current trends in microarchitecture
and explains the limits of the current approaches
and the new challenges. Section IV suggests potential microarchitectural
solutions to these challenges.
II. MICROARCHITECTURE AT A GLANCE
Microprocessor performance depends on its frequency and
IPC. Higher frequency is achieved with process, circuit, and
microarchitectural improvements. New process technology
reduces gate delay time, thus cycle time, by 1.5 times. Microarchitecture
affects frequency by reducing the amount of
work done in each clock cycle, thus allowing shortening of
the clock cycle.
Microarchitects tend to divide the microprocessor's functionality
into three major components .
• Instruction Supply: Fetching instructions, decoding
them, and preparing them for execution;
• Execution: Choosing instructions for execution, performing
actual computation, and writing results;
• Data Supply: Fetching data from the memory hierarchy
into the execution core.
A rudimentary microprocessor would process a complete
instruction before starting a new one. Modern microprocessors
use pipelining. Pipelining breaks the processing of
an instruction into a sequence of operations, called stages.
For example, in Fig. 1, a basic four-stage pipeline breaks
the instruction processing into fetch, decode, execute, and
write-back stages. A new instruction enters a stage as soon
as the previous one completes that stage. A pipelined microprocessor
with pipeline stages can overlap the processing
of instructions in the pipeline and, ideally, can deliver
times the performance of a nonpipelined one.
Pipelining is a very effective technique. There is a clear
trend of increasing the number of pipe stages and reducing
the amount of work per stage. Some microprocessors (e.g.,
Pentium Pro microprocessor ) have more than ten pipeline
stages. Employing many pipe stages is sometimes termed
deep pipelining or super pipelining.
Unfortunately, the number of pipeline stages cannot increase
• There is a certain clocking overhead associated with
each pipe stage (setup and hold time, clock skew). As
cycle time becomes shorter, further increase in pipeline
length can actually decrease performance .
• Dependencies among instructions can require stalling
certain pipe stages and result in wasted cycles, causing
performance to scale less than linearly with the number
of pipe stages.
For a given partition of pipeline stages, the frequency of the
microprocessor is dictated by the latency of the slowest pipe
stage. More expensive logic and circuit optimizations help
to accelerate the speed of the logic within the slower pipe
stage, thus reducing the cycle time and increasing frequency
without increasing the number of pipe stages.
It is not always possible to achieve linear performance increase
with deeper pipelines. First, scaling frequency linearly
with the number of stages requires good balancing of the
overall work among the stages, which is difficult to achieve.
Second, with deeper pipes, the number of wasted cycles,
termed pipe stalls, grows. The main reasons for stalls are resource
contention, data dependencies, memory delays, and
• Resource contention causes pipeline stall when an instruction
needs a resource (e.g., execution unit) that is
currently being used by another instruction in the same
• Data dependency occurs when the result of one instruction
is needed as a source operand by another instruction.
The dependent instruction has to wait (stall)
until all its sources are available.
RONEN et al.: COMING CHALLENGES IN MICROARCHITECTURE AND ARCHITECTURE 327
Out-Of-Order Execution Example
• Memory delays are caused by memory related data
dependencies, sometimes termed load-to-use delays.
Accessing memory can take between a few cycles to
hundreds of cycles, possibly requiring stalling the pipe
until the data arrives.
• Control dependency stalls occur when the control
flow of the program changes. A branch instruction
changes the address from which the next instruction
is fetched. The pipe may stall and instructions are not
fetched until the new fetch address is known.
Fig. 1 shows the impact of different pipeline stalls on the
execution flow within the pipeline.
In a 1-GHz microprocessor, accessing main memory can
take about 100 cycles. Such accesses may stall a pipelined
microprocessor for many cycles and seriously impact the
overall performance. To reduce memory stalls at a reasonable
cost, modern microprocessors take advantage of the locality
of references in the program and use a hierarchy of memory
components. A small, fast, and expensive (in $/bit) memory
called a cache is located on-die and holds frequently used
data. A somewhat bigger, but slower and cheaper cache may
be located between the microprocessor and the system bus,
which connects the microprocessor to the main memory. The
main memory is yet slower, but bigger and inexpensive.
Initially, caches were small and off-die; but over time,
they became bigger and were integrated on chip with the
microprocessor. Most advanced microprocessors today employ
two levels of caches on chip. The first level is 32-128
kB—it typically takes two to three cycles to access and typically
catches about 95% of all accesses. The second level is
256 kB to over 1 MB—it typically takes six to ten cycles to
access and catches over 50% of the misses of the first level.
As mentioned, off-chip memory accesses may elapse about
Note that a cache miss that eventually has to go to the
main memory can take about the same amount of time as
executing 100 arithmetic and logic unit (ALU) instructions,
so the structure of memory hierarchy has a major impact on
performance. Much work has been done in improving cache
performance. Caches are made bigger and heuristics are used
to make sure the cache contains those portions of memory
that are most likely to be used , .
Change in the control flow can cause a stall. The length
of the stall is proportional to the length of the pipe. In
a super-pipelined machine, this stall can be quite long.
Modern microprocessors partially eliminate these stalls by
employing a technique called branch prediction. When a
branch is fetched, the microprocessor speculates the direction
(taken/not taken) and the target address where a branch
will go and starts speculatively executing from the predicted
address. Branch prediction uses both static and runtime
information to make its predictions. Branch predictors today
are very sophisticated. They use an assortment of per-branch
(local) and all-branches (global) history information and can
correctly predict over 95% of all conditional branches ,
. The prediction is verified when the predicted branch
reaches the execution stage and if found wrong, the pipe is
flushed and instructions are fetched from the correct target,
resulting in some performance loss. Note that when a wrong
prediction is made, useless work is done on processing
instructions from the wrong path.
The next step in performance enhancement beyond
pipelining calls for executing several instructions in parallel.
Instead of "scalar" execution, where in each cycle only one
instruction can be resident in each pipe stage, superscalar
execution is used, where two or more instructions can
be at the same pipe stage in the same cycle. Superscalar
designs require significant replication of resources in order
to support the fetching, decoding, execution, and writing
back of multiple instructions in every cycle. Theoretically,
an -way superscalar pipelined microprocessor can
improve performance by a factor of over a standard
scalar pipelined microprocessor. In practice, the speedup is
much smaller. Interinstruction dependencies and resource
contentions can stall the superscalar pipeline.
The microprocessors described so far execute instructions
in-order. That is, instructions are executed in the program
order. In an in-order processing, if an instruction cannot continue,
the entire machine stalls. For example, a cache miss
delays all following instructions even if they do not need the
results of the stalled load instruction. A major breakthrough
in boosting IPC is the introduction of out-of-order execution,
where instruction execution order depends on data flow, not
on the program order. That is, an instruction can execute if its
operands are available, even if previous instructions are still
waiting. Note that instructions are still fetched in order. The
effect of superscalar and out-of-order processing is shown in
an example in Table 1 where two memory words mem1 and
mem3 are copied into two other memory locations mem2 and
Out-of-order processing hides some stalls. For example,
while waiting for a cache miss, the microprocessor can
execute newer instructions as long as they are independent
of the load instructions. A superscalar out-of-order
microprocessor can achieve higher IPC than a superscalar
in-order microprocessor. Out-of-order execution involves
dependency analysis and instruction scheduling. Therefore,
it takes a longer time (more pipe stages) to process an
328 PROCEEDINGS OF THE IEEE, VOL. 89, NO. 3, MARCH 2001
Fig. 2. Processor frequencies over years. (Source: V. De, Intel, ISLPED, Aug. 1999.)
instruction in an out-of- order microprocessor.With a deeper
pipe, an out-of-order microprocessor suffers more from
branch mispredictions. Needless to say, an out-of-order
microprocessor, especially a wide-issue one, is much more
complex and power hungry than an in-order microprocessor
Historically, there were two schools of thought on how to
achieve higher performance. The "Speed Demons" school
focused on increasing frequency. The "Brainiacs" focused
on increasing IPC , . Historically, DEC Alpha 
was an example of the superiority of "Speed Demons" over
the "Brainiacs." Over the years, it has become clear that high
performance must be achieved by progressing in both vectors
(see Fig. 4).
To complete the picture, we revisit the issues of performance
and power. A microprocessor consumes a certain
amount of energy, , in processing an instruction. This
amount increases with the complexity of the microprocessor.
For example, an out-of-order microprocessor consumes
more energy per instruction than an in-order microprocessor.
When speculation is employed, some processed instructions
are later discarded. The ratio of useful to total number
of processed instructions is . The total IPC including speculated
instructions is therefore IPC/ . Given these observations
a number of conclusions can be drawn. The energy per
second, hence power, is proportional to the amount of processed
instructions per second and the amount of energy consumed
per instruction, that is (IPC/ ) Frequency . The
energy efficiency, measured in MIPS/watt, is proportional to
. This value deteriorates as speculation increases and
One main goal of microarchitecture research is to design a
microprocessor that can accomplish a group of tasks (applications)
in the shortest amount of time while using minimum
amount of power and incurring the least amount of cost. The
design process involves evaluating many parameters and balancing
these three targets optimally with given process and
III. MICROPROCESSORS—CURRENT TRENDS AND
In the past 25 years, chip density and the associated computer
industry has grown at an exponential rate. This phenomenon
is known as "Moore's Law" and characterizes almost
every aspect of this industry, such as transistor density,
die area, microprocessor frequency, and power consumption.
This trend was possible due to the improvements in fabrication
process technology and microprocessor microarchitecture.
This section focuses on the architectural and the microarchitectural
improvements over the years and elaborates
on some of the current challenges the microprocessor industry
A. Improving Performance
As stated earlier, performance can be improved by increasingIPCand/
or frequencyorbydecreasing the instruction
count. Several architecture directions have been taken to
improve performance. Reduced instruction set computer
(RISC) architecture seeks to increase both frequency and IPC
via pipelining and use of cache memories at the expense of
increased instruction count.Complexinstruction setcomputer
(CISC) microprocessors employ RISC-like internal representation
to achieve higher frequency while maintaining lower
instruction count. Recently, the very long instruction word
(VLIW)  concept was revived with the Explicitly Parallel
Instruction Computing (EPIC) . EPIC uses the compiler
to schedule instruction statically. Exploiting parallelism staticallycanenablesimpler
control logicandhelpEPICto achieve
1) Improving Frequency via Pipelining: Process technology
and microarchitecture innovations enable doubling
the frequency increase every process generation. Fig. 2
presents the contribution of both: as the process improves,
the frequency increases and the average amount of work
done in pipeline stages decreases. For example, the number
of gate delays per pipe stage was reduced by about three
RONEN et al.: COMING CHALLENGES IN MICROARCHITECTURE AND ARCHITECTURE 329
Fig. 3. Frequency and performance improvements—synthetic model. (Source: E. Grochowski,
times over a period of ten years. Reducing the stage length
is achieved by improving design techniques and increasing
the number of stages in the pipe. While in-order microprocessors
used four to five pipe stages, modern out-of-order
microprocessors can use over ten pipe stages. With frequencies
higher than 1 GHz, we can expect over 20 pipeline
Improvement in frequency does not always improve
performance. Fig. 3 measures the impact of increasing the
number of pipeline stages on performance using a synthetic
model of an in-order superscalar machine. Performance
scales less than frequency (e.g., going from 6 to 12 stages
yields only a 1.75 times speedup, from 6 to 23 yields only 2.2
times). Performance improves less than linearly due to cache
misses and branch mispredictions. There are two interesting
singular points in the graph that deserve special attention.
The first (at pipeline depth of 13 stages) reflects the point
where the cycle time becomes so short that two cycles are
needed to reach the first level cache. The second (at pipeline
depth of 24 stages) reflects the point where the cycle time
becomes extremely short so that two cycles are needed
to complete even a simple ALU operation. Increasing the
latency of basic operations introduces more pipeline stalls
and impacts performance significantly. Please note that these
trends are true for any pipeline design though the specific
data points may vary depending on the architecture and the
process. In order to keep the pace of performance growth,
one of the main challenges is to increase the frequency
without negatively impacting the IPC. The next sections
discuss some IPC related issues.
2) Instruction Supply Challenges: The instruction
supply is responsible for feeding the pipeline with useful
instructions. The rate of instructions entering the pipeline
depends on the fetch bandwidth and the fraction of useful
instructions in that stream. The fetch rate depends on the
effectiveness of the memory subsystem and is discussed
later along with data supply issues. The number of useful
instructions in the instruction stream depends on the ISA and
the handling of branches. Useless instructions result from: 1)
control flow change within a block of fetched instructions,
leaving the rest of the cache block unused; and 2) branch
misprediction brings instructions from the wrong path that
are later discarded. On average, a branch occurs every four
to five instructions. Hence, appropriate fetch bandwidth and
accurate branch prediction are crucial.
Once instructions are fetched into the machine they are
decoded. RISC architectures, using fixed length instructions,
can easily decode instructions in parallel. Parallel decoding is
a major challenge for CISC architectures, such as IA32, that
use variable length instructions. Some implementations 
use speculative decoders to decode from several potential instruction
addresses and later discard the wrong ones; others
 store additional information in the instruction cache to
ease decoding. Some IA32 implementations (e.g., the Pentium
II microprocessor) translate the IA32 instructions into
an internal representation (micro-operations), allowing the
internal part of the microprocessor to work on simple instructions
at high frequency, similar to RISC microprocessors.
3) Efficient Execution: The front-end stages of the
pipeline prepare the instructions in either an instruction
330 PROCEEDINGS OF THE IEEE, VOL. 89, NO. 3, MARCH 2001
Fig. 4. Landscape of microprocessor families.
window  or reservation stations . The execution core
schedules and executes these instructions. Modern microprocessors
use multiple execution units to increase parallelism.
inthe instructionwindow.Theparallelism intoday'smachines
is limited by the data dependencies in the program and by
memorydelaysandresource contention stalls.
Studies show that in theory, high levels of parallelism are
achievable . In practice, however, this parallelism is not
realized, even when the number of execution units is abundant.
More parallelism requires higher fetch bandwidth, a
larger instruction window, and a wider dependency tracker
and instruction scheduler. Enlarging such structures involves
polynomial complexity increase for less than a linear performance
gain (e.g., scheduling complexity is on the order of
O of the scheduling window size ). VLIW architectures
 such as IA64 EPIC  avoid some of this complexity
by using the compiler to schedule instructions.
Accurate branch prediction is critical for deep pipelines in
reducing misprediction penalty. Branch predictors have become
larger and more sophisticated. The Pentium microprocessor
 uses 256 entries of 2-bit predictors (the predictor
and the target arrays consume 15 kB) that achieve 85%
correct prediction rate. The Pentium III microprocessor 
uses 512 entries of two-level local branch predictor (consuming
30 kB) and yields 90% prediction rate. The Alpha
21 264  uses a hybrid multilevel selector predictor with
5120 entries (consuming 80 kB) and achieves 94% accuracy.
As pipelines become deeper and fetch bandwidth becomes
wider, microprocessors will have to predict multiple
branches in each cycle and use bigger multilevel branch
prediction structures similar to caches.
B. Accelerating Data Supply
All modern microprocessors employ memory hierarchy.
The growing gap between the frequency of the microprocessor
that doubles every two to three years and the main
memory access time that only increases 7% per year impose
a major challenge. The latency of today's main memory
is 100 ns, which approximately equals 100 microprocessor
cycles. The efficiency of the memory hierarchy is highly dependent
on the software and varies widely for different applications.
The size of cache memories increases according to
Moore's Law. The main reason for bigger caches is to
support a bigger working set. New applications such as
multimedia and communication applications use larger data
structures, hence bigger working sets, than traditional applications.
Also, the use of multiprocessing and multithreading
in modern operating systems such asWindowsNT and Linux
causes frequent switches among applications. This results in
further growth of the active working set.
Increasing the cache memory size increases its access
time. Fast microprocessors, such as the Alpha or the Pentium
III microprocessors, integrate two levels of caches
on the microprocessor die to get improved average access
time to the memory hierarchy. Embedded microprocessors
integrate bigger, but slower dynamic random access memory
(DRAM) on the die. DRAM on die involves higher latency,
manufacturing difficulty, and software complexity and
is, therefore, not attractive for use in current generation
general-purpose microprocessors. Prefetching is a different
technique to reduce access time to memory. Prefetching
anticipates the data or instructions the program will access
in the near future and brings them to the cache ahead
of time. Prefetching can be implemented as a hardware
mechanism or can be instrumented with software. Many
microprocessors use a simple hardware prefetching 
mechanism to bring ahead "one instruction cache line" into
the cache. This mechanism is very efficient for manipulating
instruction streams, but less effective in manipulating data
due to cache pollution. A different approach uses ISA
extensions; e.g., the Pentium III microprocessor prefetch
RONEN et al.: COMING CHALLENGES IN MICROARCHITECTURE AND ARCHITECTURE 331
instruction hints to the hardware, to prefetch a cache line. To
implement prefetching, the microarchitecture has to support
a "nonblocking" access to the cache memory hierarchy.
C. Frequency Versus IPC
SPEC rating is a standard measure of performance based
on total execution time of a SPEC benchmark suite. Fig. 4
plots the "landscape" of microprocessors based on their
performance. The horizontal axis is the megahertz rating of
a microprocessor's frequency. The vertical axis is the ratio
of SpecINT/MHz, which roughly corresponds to the IPC
assuming instruction count remains constant. The different
curves represent different levels of performance with increasing
performance as we move toward curves in the upper
right corner. All points on the same curve represent the same
performance level, i.e., SPEC rating. Performance can be
increased by either increasing the megahertz rating (moving
toward the right) or by increasing the SpecINT/MHz ratio
(moving toward the top) or by increasing both. For a given
family of microprocessors with the same ISA (and, hence,
the same instruction count), the SpecINT/MHz ratio is
effectively the measure of their relative IPC.
For example, let us examine the curve that represents the
Intel IA32 family of microprocessors. The first point in the
curve represents the Intel386 microprocessor. The next point
represents the Intel486 microprocessor. The main improvement
between these two microprocessors is due to the improvement
of IPC. This is obtained through the pipelined design
of the Intel486 microprocessor and the introduction of
the L1 cache.
The Pentium microprocessor was the first superscalar
machine Intel introduced; it also featured branch prediction
and split caches. Performance gain came from parallelism
as well as reduced stalls. Subsequent proliferation of the
Pentium microprocessor involved frequency boosts and
relatively small microarchitectural modifications, leaving
the IPC almost unchanged.
The next level of performance appears with the Pentium
Pro microprocessor. This microprocessor, followed later by
the Pentium II and Pentium III microprocessors, is a deeply
pipelined superscalar out-of-order microprocessor, which simultaneously
improved both frequency and IPC.
Other families of microprocessors show similar trends.
New microarchitecture can boost both IPC and frequency
while new process technology typically boosts frequency
only. In some cases (see the Alpha 21 064), higher frequency
may even reduce IPC, but overall performance is still
D. Power Scaling
Each new microprocessor generation introduces higher
frequency and higher power consumption. With three times
less energy per gate switch and 1.5 times frequency increase,
simple shrinking of a microprocessor to a new process
can reduce power consumption close to two times. A new
microarchitecture, on the other hand, increases work per
Fig. 5. Maximum power consumption. (Source: S. Borkar .)
Fig. 6. Power density evolution. (Source: S. Borkar .)
instruction and wastes energy on wrongly speculated instructions,
hence reducing energy efficiency and increasing
the total power consumption of the microprocessor.
Fig. 5 supports the above observation. When a new generation
of microprocessors is introduced, it consumes twice
the power of the previous generation using the same process.
After a while, the microprocessor moves to a new process
and improves both power and frequency. Extrapolation of
the power dissipation numbers suggests that microprocessors
may consume 2000Win the near future! Up until now, power
density was handled using packaging solution to dissipate
heat. Fig. 6 indicates that power density will soon become a
major problem. Packaging alone will not be able to address
power density in a cost-effective manner. Note that chips in
0.6- m technology used to have power density similar to a
(cooking) hot plate (10 W/cm ). If the trend continues, soon
we may experience a chip with the power density of a nuclear
power plant or even a rocket nozzle. Furthermore, local
hot spots have significantly higher power density than the average,
making the situation even worse. Potential solutions to
the power density problems are addressed in Section IV.
E. Application Specific Enhancements
The energy crisis encourages the development of application-
specific enhancements (ASEs), also termed
332 PROCEEDINGS OF THE IEEE, VOL. 89, NO. 3, MARCH 2001
focus-MIPS, which aim to achieve better performance and
better MIPS for specific classes of applications. Intel's
MMX technology and streaming single instruction multiple
data (SIMD) extensions are good examples. These SIMD
extensions allow a single instruction to perform the same
operation on two, four, or eight data elements at the same
time, potentially improving performance and reducing
power appropriately. Other microprocessor manufacturers
have introduced similar extensions—AMD 3DNOW! ,
Sun VIS , Motorola Altivec  technology, and more.
F. Coming Challenges
Until recently, microarchitects and designers used the
increased transistor budget and shorter gate delay to focus
mainly on developing faster, bigger, and hotter microprocessors.
Lower cost and lower power microprocessors were
made available by waterfalling—a process shrink that takes
yesterday's hot and expensive microprocessor and makes
it today's low-power and inexpensive microprocessor. This
will change. Power consumed by future high-performing
microprocessors will reach levels that cannot suit mobile
computers even after a process shrink.
Single thread performance does not scale with frequency
and area. Memory latencies and bandwidth do not scale
as well either, slowing down memory-bound applications.
Deeper pipes allow higher frequency at the cost of longer
stalls due to branch misprediction and load-to-use delays. A
design challenge is to minimize stalls while still increasing
instruction-level parallelism (ILP).
Controlling power becomes a major challenge. Process
technology reduces the switching power of a single device,
but higher frequency and increased device density increase
power dissipation at a faster rate. Power density is reaching
levels close to nuclear reactors. Power is becoming the
limiting factor in microprocessor design. Microarchitecture
should help reduce power.
ASE may be a good power efficient design alternative
but at a cost. Developing ASEs requires a lot of work in
defining new architectures—writing new development tools
and porting existing applications to the new architecture.
With frequencies in excess of 1 GHz, even moving at the
speed of light, signals can only travel a short distance. In the
future, it may take several cycles for a signal to travel across
the chip. Wire delays exhibit an inherent conflict of size and
time: bigger structures that may improve performance (e.g.,
caches and branch predictors) may take longer to access. Incorporating
large structures that provide higher ILP without
sacrificing frequency will be one of the main challenges.
IV. FUTURE DIRECTIONS
In this section, we present potential directions and solutions
to address these challenges. Many solutions often require
difficult tradeoffs. In particular, we can exploit more
performance, but at the cost of power and area. While numerous
techniques are proposed, the applicability of a certain
technique to a certain market segment has to be examined according
to the intended usage of the microprocessor.
A. Single Thread Performance Improvements
As stated earlier, performance is dictated by frequency
and IPC. Process technology is likely to continue to enable
frequency scaling close to 1.5 times per generation. Scaling
frequency beyond process advancement requires reduction
in the number of logic levels per pipe stage. This number
is already low and reducing it further is a complex task
with questionable gain. Certain operations, usually those
with a very short dependency path, do benefit from higher
frequency. Other operations scale more easily by running
several of them in parallel rather than by trying to pipeline
them or run them faster, even at the cost of duplicating the
logic. To address this, there may be different clock domains
on the same microprocessor. Simple execution units (e.g.,
adders) may run at a higher frequency, enabling very fast
results forwarding to dependent instructions, thus cutting
the program critical path and improving performance. Other
operations such as decoding multiple RISC instructions are
more easily implemented using slower parallel decoders
than faster pipelined ones.
Many techniques have the potential to increase IPC. Techniques
are described according to the function they influence—
instruction supply, execution, and data supply.
1) Instruction Supply: Instruction supply can be improved
by reducing the number of stalls (no instructions are
fetched) and by increasing the fetch bandwidth (the number
of instructions fetched per cycle). The major sources
for stalls are instruction cache misses and mispredicted
branches. Instruction cache stalls can be addressed by
general memory hierarchy optimization methods. Some
relevant techniques are mentioned under data supply later.
Mispredicted branches create long stalls. Even at 5% misprediction
rate, shaving an additional percent off may improve
performance of a deeply pipelined microprocessor by
4%! More sophisticated adaptive or hybrid branch predictors
have been proposed .
Accepting that some branches will be mispredicted, several
mechanisms have been proposed to reduce the misprediction
penalty. One direction is to reduce the length of the
pipeline by maintaining a decoded instruction cache. Especially
for the IA-32 architecture, eliminating the decoding
stage following a misprediction can reduce stall penalty significantly.
A similar, but potentially more cost-effective approach
suggests holding a decoded version of the alternate
program path only.
A more ambitious idea, termed eager execution, suggests
executing both the taken and the not taken paths of difficult
to predict branches . When the branch is resolved,
the wrong path is flushed. Eager execution should use
a confidence mechanism to determine the likelihood of
a branch to be mispredicted . Eager execution may
boost performance, but it is a very inefficient technique.
Both paths are executed and consume power with only
one doing useful work. Moreover, the ability to perform
RONEN et al.: COMING CHALLENGES IN MICROARCHITECTURE AND ARCHITECTURE 333
both paths without performance penalty requires additional
resources—increasing cost and complexity.
Another goal is to increase the number of fetched instructions
per cycle. To do that in a sustainable manner, one needs
to fetch more than one contiguous basic block at a time. The
initial attempt is to try to perform multiple branch predictions
and multiple block prefetching. These techniques increase
fetch bandwidth but involve multiporting and complex
steering logic to arrange instructions coming from several
different cache lines. Trace caches, block caches, and
extended basic block caches (XBCs) were proposed as better
alternatives that provide both high bandwidth and low latencies
-. These cache-like structures combine instructions
from various basic blocks and hold them together. Optionally,
the instructions can be stored in decoded form to
reduce latency. Traces are combined based on runtime behavior.
Traces take time to build but, once used, exhibit very
fast fetching and execution on every usage.
Several studies are extending the concept of trace caches
and annotate or reorder instructions within the traces (scheduled
trace cache). This enables faster dependency tracking
and instruction scheduling, gaining higher execution bandwidth
and lower latency at the cost of more complicated trace
building and less flexibility in accessing portions of the trace
Instruction fusion reduces the amount of work by treating
several (e.g., two) instructions as a single combined instruction,
best thought of as carpooling. The longer the two instructions
can travel together in the pipeline, fewer resources
are needed and less power is consumed. In particular, in cases
where we can build an execution unit that can execute dependent
fused instructions together, we can reduce the program
critical path and gain performance .
2) Execution: Increased instruction-level parallelism
starts with a wider machine. More execution units, a larger
out-of-order instruction window, and the ability to process
more dependency tracking and instruction scheduling per
cycle are required, but are not enough. With out-of-order
execution, the main limiting factor is not lack of resources;
it is data dependency imposed by the data flow among
For years, data flow was perceived as a theoretical barrier
that cannot be broken. Recently, several techniques, collectively
termed as beyond data flow, showed that this barrier
can be broken, resulting in more parallelism and increased
performance. Some of these techniques use super speculation:
they predict results, addresses, or relations among instructions
to cut dependencies.
Value prediction - is probably the best example in
this domain. Value prediction tries to predict the result of an
incoming instruction based on previously computed results
and, optionally, the program path (control flow) leading to
that instruction. When a prediction is made, the instructions
that depend on that result can be dispatched without waiting
for the actual computation of the predicted result. The actual
computation is done for verification only. Of course, when
the prediction is wrong, already executed dependent instructions
must be reexecuted. Since mispredictions cost both performance
and power, the rate of misprediction has to be minimized.
While one would assume that the probability of predicting
a value out of 2 or 2 potential values is rather low, studies
show that about 40% and more of the results can be correctly
predicted. The simplest predictor is a last-value predictor,
which predicts that the last result of an instruction will also
be the result when the same instruction is executed again.
More sophisticated predictors that identify patterns of values
achieve even higher prediction rate.A40% correct prediction
rate does not mean that the other 60% are wrong. Confidence
mechanisms are used to make sure we predict only those instructions
that are likely to be predicted correctly, reducing
the number of mispredictions to 1%-2% and less.
Similarly, memory-address prediction predicts the
memory address needed by a load instruction in case the
load address cannot be computed since, for example, it
contains a register that is not known yet. When the predictor
provides an address with reasonable confidence, the load can
be issued immediately, potentially cutting the load-to-use
delay. Predicting store addresses can also improve performance.
Several address predictors were developed to predict
various common address patterns .
Instruction reuse is a family of nonspeculative techniques
that try to trade off computation with table lookup. The
simplest (and oldest) form of this technique is value cache
, where long latency operations (e.g., divide) are cached
along with their operands and results. Future occurrences
are looked up in the cache for match before execution. The
more interesting form of instruction reuse  tries to examine
a sequence of incoming instructions along with their
combined input data to produce the combined result without
actually computing it. The technique is nonspeculative and,
thus, can provide both performance and power reduction,
but it does require large amount of storage.
Memory addresses are computed at execution time, thus
dependency among memory accesses is not simple to determine.
In particular, the microprocessor cannot determine
whether a certain load accesses the same address as a previous
store. If the addresses are different, the load can be
advanced ahead of the store. If they are the same, the store
can forward the result directly to the load without requiring
the data to be written first to memory. Microprocessors today
tend to take the conservative approach in which all previous
store addresses have to be known before the load can be issued.
Memory disambiguation techniques  have been devised
to predict, with high confidence, whether a load collides
with a previous store to allow advancing or value forwarding.
Memory disambiguation can be taken a step forward. If the
exact load-store pair can be predicted, the load can bypass the
store completely, taking its data directly from the producer
of the value to be stored. Furthermore, in such a case, the
consumers of the load, that is, the instructions that use the
loaded value can also take their operands directly from the
producer, bypassing both the store and loads and boosting
performance significantly . A generalized version of this
concept, termed unified renaming, uses a value identity de-
334 PROCEEDINGS OF THE IEEE, VOL. 89, NO. 3, MARCH 2001
tector to determine whether two future results will actually
be the same . If so, all references to the later result are
converted to reference the former result, thus collapsing the
program critical path. Loads following stores are covered by
this technique since the load result is identical to the source
of the store result.
Register tracking can also be used to avoid dependencies.
For example, in the IA-32 architecture, the stack pointer is
frequently changed by push/pop operations that add/subtract
a constant from the stack pointer value. Every push/pop depends
on all previous push/pops. The register tracking mechanism
computes the stack pointer values at decode, making
them all independent of one another .
3) Data Supply: Probably the biggest performance challenge
today is the speed mismatch between the microprocessor
and memory. If outstanding cache misses generated by
a single thread, task, or program can be grouped and executed
in an overlapped manner, overall performance is improved.
The term memory-level parallelism (MLP)  refers to the
number of outstanding cache misses that can be generated
and executed in an overlapping manner. Many microarchitectural
techniques have been devised to increase MLP.
Many of the techniques mentioned in the execution
section above increase MLP. Value prediction, memory
address prediction, and memory disambiguation enable
speculative advancing of loads beyond what a conservative
ordering would allow, potentially resulting in overlapping
loads. Load-store forwarding and unified renaming eliminate
dependencies among memory operations and increase
memory parallelism. Surely, more resources such as a larger
instruction window, more load/store execution units, and
more memory ports will increase MLP.
A different technique to increase MLP is data prefetching.
Data prefetching attempts to guess which portions of
memory will be needed and to bring them into the microprocessor
ahead of time. In floating-point and multimedia
applications, the memory access pattern consists of several
simple contiguous data streams. Special hardware tracks
memory accesses looking for such patterns . When
detected, an access to a line so patterned triggers the fetch
of the next cache line into an ordinary cache or a prefetch
buffer. More sophisticated techniques (e.g., context-based
prefetching ) can also improve other types of applications.
Prefetching algorithms try to consider the specific
memory-paging behavior [synchronous DRAM (SDRAM),
rambus DRAM (RDRAM)] to optimize performance and
power. Prefetching costs extra hardware, may overload the
buses, and may thrash the cache. Thus, prefetch requests
should be issued only when there is enough confidence that
they will be used.
Embedded DRAM and similar process/circuit techniques
attempt to increase the amount of memory on die. If
enough on-die memory is available, it may be used not
only as a cache, but also as a faster memory. Operating
system/application software may adapt known methods
from networked/microprocessor/nonuniform memory access
systems to such architectures. Big memory arrays, to
a certain degree, are also considered more energy efficient
than complex logic. Since memories are more energy
efficient than logic, DRAM on die may become an attractive
feature in the future.
Of course, much effort is being put into improving traditional
caches.With the increase in microprocessor frequency,
more levels of memories will be integrated on the die; on the
other hand, the size of the L1 cache will be reduced to keep up
with the ultrahigh frequency. Such memory organization may
cause a coherency problem within the chip. This problem
may be resolved using similar methods that resolve the coherency
problem in a multiprocessor (MP) system today. In
addition, researches will continue to look for better replacement
algorithms and additional structures to hold streaming
data so as to eliminate cache thrashing.
4) Pushing ILP/MLP Further: Previous sections discuss
ways to increase parallelism in a single thread. The next level
of parallelism is achieved by running several threads in parallel,
thus achieving increased parallelism at a coarser grain
level. This section briefly covers several ideas that aim at
speeding up single thread performance by splitting it into
several threads. Improving parallelism by running several independent
threads simultaneously is addressed at the end of
Selective preprocessing of instructions was suggested to
reduce branch misprediction and cache miss stalls. The idea
is to use a separate "processor" or thread to run ahead of
the actual core processor and execute only those instructions
that are needed to resolve the difficult-to-predict branches
or compute the addresses of loads which are likely to cause
cache misses. Early execution of such loads acts very much
like data prefetch. Precomputed correct control information
is passed to the real thread, which can reduce the number of
Dynamic multithreading  attempts to speculatively execute
instructions following a call or loop-end along with the
called procedure or the loop body (thread speculation). Dependencies
among the executed instruction blocks may be
circumvented, by value prediction.With sufficient resources,
useful work can be done speculatively. Even if some of the
work is found erroneous and has to be redone, prefetching
side effects may help performance.
The multiscalar technique  extends the concept of
speculative threads beyond dynamic multithreading. The
classic multiscalar idea calls for the help of the user and/or
the compiler to define potential threads and to provide
dependency information. A thread is ideally a self-contained
piece of work with as few inputs and outputs as possible.
A separate mechanism verifies the user/compiler provided
dependencies (e.g., memory conflicts).
B. Process Technology Challenges
Besides pushing the performance envelope of a
single-thread instruction stream, microarchitects are
faced with some process technology related challenges.
1) Power Challenge: So far we have mainly considered
the active power and ignored the leakage power. Leakage
power is generated by current running in gates and wires
RONEN et al.: COMING CHALLENGES IN MICROARCHITECTURE AND ARCHITECTURE 335
even when there is no activity. There are three leakage
sources: 1) the subthreshold drain-source leakage; 2)
junction leakage; and 3) gate-substrate leakage. To gain
performance, lower threshold voltage ( ) is used. Lower
, however, increases leakage power (subthreshold). Also,
higher power densities increase device temperature, which
in turn increases leakage power (junction). As technology
scales, oxide thickness is also scaled which causes more
leakage power (gate). As such, controlling power density is
becoming even more crucial in future deep submicron technologies.
When adding complexity to exploit performance,
one must understand and consider the power impact. In
addition, one needs to target the reduction of standby power
dissipation. There are three general approaches to power
reduction: 1) increase energy efficiency; 2) conserve energy
when a module is not in use; and 3) recycle and reuse.
Following are several possible suggested solutions.
Trading frequency for IPC is generally perceived as
energy efficient. Higher IPC with lower frequency and a
lower voltage can achieve the same performance but with
less power. This is not always accurate. Usually, higher IPC
requires additional complexity that involves additional work
to be done on each instruction (e.g., dependency tracking).
Moreover, many instructions are speculatively processed
and later discarded or reexecuted (e.g., wrong data was
predicted). Some performance-related techniques (e.g., load
bypassing) do reduce the work performed per instruction
and thus are more energy friendly. Better predictors with
confidence-level evaluation can be combined to control
speculation and avoid the waste of energy.
It is common sense to shut down circuits when they are not
in use to conserve energy. Shutting down logic and circuits
can be applied to part or the whole microprocessor. However,
the sharp current swings associated with frequent gating
cause " " noise. This noise is also referred to as the simultaneous
switching noise and is characterized by the equation:
, where is the voltage fluctuation,
is the inductance of the wire supplying the current, is the
current change, and is the rise or fall time of gates. One potential
area of research is to determine the right time to shut
down blocks to avoid sharp current swings. Prediction algorithms
based on program dynamic behavior may be useful.
Dynamic voltage/frequency scaling  can reduce
energy consumption in microprocessors without impacting
the peak performance. This approach varies the microprocessor
voltage or frequency under software control to
meet dynamically varying performance requirements. This
method conserves energy when maximum throughput is
not required. Intel's SpeedStep technology  and Transmeta's
LongRun technology ,  both use dynamic
Some microarchitecture ideas can actually reduce the
number of instructions  processed, thus saving energy.
Instruction reuse, mentioned earlier, saves previously executed
results in a table and can trade repeated execution
with table lookup, potentially increasing performance and
2) Wire Delays: Wiring is becoming a bottleneck for frequency
improvement. Wires do not scale like gates. Wire
delay depends on the wire resistance, which is inversely proportional
to the cross section of a wire, and the wire spacing
distance causing the coupling capacitance between sides of
adjacent wires. If all dimension of a wire are scaled (width,
thickness, and length), wire delay remains fixed (in nanoseconds)
for the scaled length . Incr