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Xu hướng công nghệ IC và Kiến trúc máy tính

• Goal of this class:

CS5100 Advanced Computer Architecture

− To understand the trends of IC technology and be able to
relate the trends with computer architecture designs
− Why need to know the trends?

Technology Trends

• Learn from the history
• Understand possible future and know how to adapt now

Prof. Chung-Ta King

• Class outline:

Department of Computer Science
National Tsing Hua University, Taiwan

− Trends in technology (Sec. 1.4)
− Trends in power and energy (Sec. 1.5)
− Trends in cost (Sec. 1.6)

National Tsing Hua University

National Tsing Hua University

IC Technology and Processor Performance

Review of Transistors (MOSFET) on IC

# transistors on ICs x2 every 2 years

Exponential growth

Source: Intel

National Tsing Hua University

National Tsing Hua University

Technology Scaling
• Feature size:

− Minimum size of transistor or wire in x or y
− 10 microns in 1971 to 22 nm in 2012
− New technology node every 2 years or so
− ~70% (S) reduction for each generation

National Tsing Hua University

10 µm – 1971
6 µm – 1974
3 µm – 1977
1.5 µm – 1982
1 µm – 1985
800 nm – 1989
600 nm – 1994
350 nm – 1995
250 nm – 1997
180 nm – 1999
130 nm – 2001
90 nm – 2004
65 nm – 2006
45 nm – 2008
32 nm – 2010
22 nm – 2012
14 nm – 2014
10 nm – 2016

Effects of Scaling
• More transistors per unit area

− Feature size reduced by 0.7 (S) area of a transistor
reduced by 0.5 (S2)
− 2X # transistors/unit area
− Fixed cost per wafer lower cost per transistor

• Faster transistors

− Reduce time to switch on/off transistors
speed improved by S
exponential increase in clock rate

• Less supplied voltage and power

− Power to switch transistor reduced, but not power density
− Voltage to drive transistors reduced

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Effects of Scaling

Summary: Technology Trends

• Local wires are getting faster
• Global wires are getting slower, i.e. scale poorly

• Integrated circuit technology

− Transistor density: 35%/year
− Die size: 10-20%/year
− Integration overall: 40-55%/year (slow down after 2003!)

− No longer possible to cross chip in one cycle
− Computer architects need to plan around this

• DRAM capacity: 25-40%/year (slowing)
• Flash capacity: 50-60%/year

Chip size

− 15-20X cheaper/bit than DRAM

Scaling of
reachable radius

3D stacking
Distributed mechanisms

• Magnetic disk capacity: 40%/year
− 15-25X cheaper/bit than Flash
− 300-500X cheaper/bit than DRAM
− But not speed

National Tsing Hua University

National Tsing Hua University

Implications for Computer Architecture

Bandwidth versus Latency

• High rate of density improvements

• Bandwidth or throughput

− Used for bringing 4-bit, 8-bit, through 64-bit
microprocessors in the early days of microprocessors
− Used for multiprocessor per chip, wider SIMD, …, in recent

• Quantitative changes leading to qualitative changes
− 25K to 30K transistors per chip in early 1980s
possible to build a single-chip 32-bit microprocessor
− By mid 1980s, FP unit can be integrated
− By late 1980s, L1 cache can fit on the same chip
Performance improvements often in discrete steps

− Total work done in a given time
− 10,000-25,000X improvement for processors
− 300-1200X improvement for memory and disks

• Latency or response time

− Time between start and completion of an event
− 30-80X improvement for processors
− 6-8X improvement for memory and

• Work with signal propagation delay on wires

National Tsing Hua University

National Tsing Hua University

Bandwidth and Latency

Summary: Bandwidth and Latency
• For disk, LAN, memory & microprocessor, bandwidth
improves by square of latency improvement
Log-log plot
and latency

− In the time that bandwidth doubles, latency improves by
no more than 1.2X to 1.4X

• Lag probably even larger in real systems, as BW gains
multiplied by replicated components

Multiple processors in a cluster or in a chip
Multiple disks in a disk array
Multiple memory modules in a large memory
Simultaneous communication in switched LAN

• HW and SW developers should innovate assuming
latency lags bandwidth
National Tsing Hua University

National Tsing Hua University


Power Density Trend

• Trends in technology (Sec. 1.4)
• Trends in power and energy (Sec. 1.5)
• Trends in cost (Sec. 1.6)

P = αCVdd f + Vdd I st + Vdd I leak

Source: Intel Corp.
National Tsing Hua University

National Tsing Hua University


Power and Energy

• Intel 80386 consumed ~2 W, but 3.3 GHz Intel Core
i7 consumes 130 W
• Heat must be
dissipated from
the chip
• Today, power is
major limitation
to using
transistors, not
silicon area

• Pavg = Pdynamic + Pstatic
• Energy is related to power through time
• If power dissipation remains constant through time
T, then
E = (Pavg x T)

National Tsing Hua University

National Tsing Hua University

Dynamic Power and Energy

Static Power

• For CMOS chips, traditional dominant energy
consumption has been in switching transistors,
called dynamic power

• Because leakage current flows even when a
transistor is off, now static power is important too

− Currentstatic x Voltage
− Scales with number of transistors
− Increase as transistors shrink and # transistors increases

− ½ x capacitive load x voltage2 x frequency switched

• For mobile devices, energy is better metric

• With 65nm or better technologies, leakage can account for
50% of total power if not designed properly

− ½ x capacitive load x voltage2

• Reducing clock rate reduces power, but not energy
• Reducing power:

− To reduce: power gating

Do nothing well: turn off clock of inactive modules
Dynamic Voltage-Frequency Scaling (DVFS)
Low power state for DRAM, disks
Overclocking, turning off cores

National Tsing Hua University

National Tsing Hua University

Implications for Computer Architecture


• Architectural designs for low power using metrics
such as tasks per joule or performance per watt

• Trends in technology (Sec. 1.4)
• Trends in power and energy (Sec. 1.5)
• Trends in cost (Sec. 1.6)

− Use the right power/energy to do the right things

• Sometimes, do things faster but at a higher power
may be better race to halt
− Often techniques for performance also lead to power

National Tsing Hua University

National Tsing Hua University

VLSI Economics


• Selling price Stotal

• Engineering cost

− Stotal = Ctotal / (1-m)

− Depends on size of design team, including benefits,
training, computers
− CAD tools:

• m = profit margin
• Ctotal = total cost

• Digital front end: $10K
• Analog front end: $100K
• Digital back end: $1M

− Nonrecurring engineering cost (NRE)
− Recurring cost
− Fixed cost: data sheets and application notes, marketing
and advertising, yield analysis

• Prototype manufacturing

− Mask costs: $500k – 1M in 130 nm process
− Test fixture and package tooling

National Tsing Hua University

National Tsing Hua University

Recurring Cost of IC

Cost and Computer Architecture
• Sole control of computer architects on IC cost is die
area, and hence a portion of the cost
− What functions should be included or excluded in the
− Number of I/O pins
− Design complexities

− Defects per unit area = 0.016-0.057 defects per cm2 (2010)
− N = process-complexity factor = 11.5-15.5 (40 nm, 2010)

National Tsing Hua University

National Tsing Hua University

Technology and Architecture

Technology and Architecture

• How to translate technology improvements into
increases in computing performance?

• Increased transistor counts:

− Basic strategies: parallelism, speculation, overlapping,
− Modular and hierarchical architectures

− Constraints on power dissipation, localized
communication, design and verification complexities

• Increasing clock frequency:

− Need to tackle power, heat, clock skew, wire delay
− Gap to memory and I/O devices, PC board design
multi-level cache (with on-chip cache)
− Need scalable design with little complexity, parallelism
e.g., multiple functional units, RICS cores
− Need good locality, avoid long distance and rapid
interaction, e.g., MP on a chip

• Shorter wires, lower complexity, scale with technology
• On-chip cache/DRAM, MP on a chip, multithreading, vector
processing, VLIW

− For monitoring and learning program’s execution and
subsequently recasting it for faster execution
− Self-adapting, self-management, self-healing, …
− More functionalities: multimedia, facilities for I/O and
memory, bandwidth and latency improvement

National Tsing Hua University

• Trends in technology
• Trends in power and energy
• Do you understand the trends of IC technology?
• Can you explain the implications and relate the
trends with computer architecture designs?

National Tsing Hua University

National Tsing Hua University

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