The early 21c has brought the power of the computer into the hands of the general population, and though these computers consume small amounts of energy they are so numerous that their Energy Efficiency will soon become a major issue. This presentation looks at modern Computing, the ways that Energy Efficiency is currently being enhanced, and the principles behind this.

1. Energy Efficient Computing ... In the early 21C

Abstract:


Opinions expressed are those of the author alone
With the assistance of its global partners, ARM shipped 8.7 billion CPUs in 2012; a number which continues
to grow at around ~20%pa. The 40B we have shipped to date outnumber the total of PC's more than 50
times; and today more than 75% of the things connected to the Internet are ARM based. The dominant
nature of Computing in the 21c is very different to that of the Mainframe era. It is sobering to think that if
each of those 8.7B CPUs was to dissipate just 100mw, then it would require the output of two modern power
stations to drive them; with 2.4 next year, and 3 the year after that! So Electronic Systems are also defining
where the real Energy Efficient Computing issue is! But with such a small footprint it must be easy to
measure and manage power optimisation? An increasing percentage of these are immensely complex
systems, running significant multi-tasking and multi-threaded operating systems on platforms which include
multi-processor CPU/GPU configurations, and GB of memory. Whilst their minimum dissipations are a few
uW, their peak power exceed the silicon's ability to dissipate it; so the penalty for power un-aware software
design is huge. What has been done to manage this in Electronic Systems design, and can any lessons can be
transferred to the Classic Computing domains?
Context



1hr talk at The Centre for Robotics and Neural Systems (CNRS) at University of Plymouth, Devon, UK.
The CRNS has a regular seminar series inviting national and international speakers.
http://www.tech.plym.ac.uk/SOCCE/CRNS/
SlideCast and pdf available via http://ianp24.blogspot.co.uk/
1

2. Opinions expressed are those of the author alone
Prof. Ian Phillips
Principal Staff Eng’r,
ARM Ltd
ian.phillips@arm.com
Visiting Prof. at ...
Contribution to Industry
Award 2008
Centre for Robotics and Neural Systems
Uo.Plymouth
1nov13
SlideCast and pdf available via http://ianp24.blogspot.co.uk/
2
1v0

3. Energy Efficient Computing ..?
3

4. Energy Efficient Computing ..?
4

5. Energy Efficient Computing ..?
5

6. The Visible Face of Computing Today
6

7. The Invisible Face of Computing Today
 100’s of Billions of computers each consuming mW!
 Bringing Embedded Intelligence to the Consumer
Market, has changed the Face of Computing! (Again)
7

8. Our 21c World ...
8

9. Markets provide the Growth Drivers
3rd Era
Millions of Units
Computing as part
of our lives
2nd Era
Broad-based computing
for specific tasks
1st Era
Select work
tasks
1960
1970
1980
1990
2000
2010
2020
Today: ~2% of our Energy Use goes on Computing and Electronics!
... Tomorrow: It could easily be 20%!
9

10. ARM in the Digital World
150+
billion
CPUs cumulative
by 2020
 8.7B CPUs shipped in 2012 (Growing 20%pa.pa)
 75% of the things connected to the
Internet today are ARM Powered! Gartner
40+
billion
CPUs to date
1998
10
http://www.arm.com/
2012
2020

11. Moore’s Law ...
X
100nm
10um
Transistor/PM (K)
1um
Transistors/Chip (M)
Approximate Process Geometry
10nm
Gordon Moore. Founder of Intel. (1965)
100um
ITRS’99
...
11
http://en.wikipedia.org/wiki/Moore’s_law
x More Functionality on a Si Chip in 20 yrs!

12. A Machine for Computing ...
Computing: A general term for algebraic manipulation of data ...
Numerated
Phenomena
IN (x)
y=F(x,t,s)
Processed Data/
Information
OUT (y)
... State and Time are always factors (variable weight).

It can include phenomena ranging from human thinking to calculations
with a narrower meaning.
Usually used it to exercise analogies (models) of real-world situations;
Frequently in real-time (Fast enough to be a stabilising factor in a loop).
Wikipedia

... So what part does Hardware and Software play?
... And what about Energy?
12

13. Antikythera c87BC ... Planet Motion Computer
Mechanical
Technology
• Inventor: Hipparchos (c.190 BC – c.120 BC).
•
Ancient Greek Astronomer, Philosopher and Mathematician.
Single-Task, Continuous Time, Analogue Mechanical Computing (With backlash!)
See: http://www.youtube.com/watch?v=L1CuR29OajI
13

14. Orrery c1700 ... Planet Motion Computer
Mechanical
Technology
• Inventor: George Graham (1674-1751). English Clock-Maker.
• Single-Task, Continuous Time, Analogue Mechanical Computing (With backlash!)
14

15. Babbage's Difference Engine 1837
Mechanical
Technology
(Re)construction
c2000

The difference engine consists of a number of columns, numbered from 1 to N. Each column is able to store one decimal number. The only operation the engine
can do is add the value of a column n + 1 to column n to produce the new value of n. Column N can only store a constant, column 1 displays (and possibly prints)
the value of the calculation on the current iteration.
Computer for Calculating Tables: A Basic ALU Engine
15

16. “Enigma” c1940
Mechanical
Technology
Data Encryption/Decryption Computer
16

17. “Colossus” 1944
Valve/Mechanical
Technology
Code-Breaking Computer: A Data Processor
17

18. “Baby” 1947
(Reconstruction)
Valve/Software
Technology
General Purpose, Quantised Time and Data, (Digital) Electronic Computing
18

19. Signal Processing
Tele-Verta Radio
4 Valves
1 Rectifier Valve
BTH
Crystal Set
c1945
1 Diode
Evoke DAB Radio
c1925
100 M Transistors
2-3 Embedded Processors
Bush Radio
7 Transistors
1 Diode
c1960
19
c2005

20. Radio as Computation ...
Vi
Vrf=Vi*100
Vro='Bandpass'(Vif*1000)
Vrf
Vif
Vro
Vif=Vrf*Vlo
Vlo
Vlo=Cos(t*1^6)
Single-Task (Embedded), Real-Time, Analogue (Close-Enough) Computing
20

21. Radio as Computation ...
Valve
Technology
Vi
Vrf=Vi*100
Vro='Bandpass'(Vif*1000)
Vrf
Vif
Vro
Vif=Vrf*Vlo
Vlo
Vlo=Cos(t*1^6)
Single-Task (Embedded), Real-Time, Analogue (Close-Enough) Computing
21

22. Radio as Computation ...
‘Integrated Circuit’
Transistor
Valve
Technology
Vi
Vrf=Vi*100
Vro='Bandpass'(Vif*1000)
Vrf
Vif
Vro
Vif=Vrf*Vlo
Vlo
Vlo=Cos(t*1^6)
Single-Task (Embedded), Real-Time, Analogue (Close-Enough) Computing
22

23. Computing is Era and Application Related ...
Computing: Creating Useful Output from Input ...
Architecture: The way this is done on the day.
It is the Most Important Product Decision!
(HW, SW, Digital, Analogue, Optics, Graphene, Mechanics, Steam, etc)
23

24. Moore's Real Law: x2 Functionality Every 18mth!
 Cascade of Technologies supporting Functional growth ...
Functional Density (units)
1012
1010
106
102
Electronic era:
System era:
1975-2005
2003-2030
100
1960
1980
2000
2020
... The ‘Law’ started with Wood ⇒ Stone ⇒ Bronze ⇒ Iron
24

25. Computing in a Cool iCon ...
25

26. ‘A lot’ of Architecture in a Smart Phone ...
... Computation in many forms
26

27. Take a Look Inside...
Level-1: Modules
The Control Board.
27
http://www.ifixit.com

28. Inside The Control Board
(a-side)
Level-2: Sub-Assemblies


Visible Computing Contributors ...
 Samsung: Flash Memory - NV-MOS (ARM Partner)
 Cirrus Logic: Audio Codec - Bi-CMOS (ARM Partner)
 AKM: Magnetic Sensor - MEM-CMOS
 Texas Instruments:Touch Screen Controller and mobile DDR - Analogue-CMOS (ARM Partner)
 RF Filters - SAW Filter Technology
Invisible Computing Contributors ...
 OS, Drivers, Stacks, Applications, GSM, Security, Graphics, Video, Sound, etc
 Software Tools, Debug Tools, etc
28
http://www.ifixit.com

29. Inside The Control Board
(b-side)
Level-2: Sub-Assemblies

More Visible Computing Contributors ...






A4 Processor. Spec:Apple, Design & Mfr: Samsung
Digital-CMOS (nm) ...
 Provides the iPhone 4 with its GP computing power.
 (Said to contain ARM A8 600 MHz CPU and other ARM IP)
ST-Micro: 3 axis Gyroscope - MEM-CMOS (ARM Partner)
Broadcom: Wi-Fi, Bluetooth, and GPS - Analogue-CMOS (ARM Ptr)
Skyworks: GSM
Analogue-Bipolar
Triquint: GSM PA Analogue-GaAs
Infineon: GSM Transceiver - Anal/Digi-CMOS (ARM Partner)
GPS
Bluetooth,
EDR &FM
29
http://www.ifixit.com

30. Level-3: Processor
NB: The Tegra 3 is similar to the
A4/5, but not used in the iPhone
30
(Nvidea Tegra 3, Around 1B transistors)

31. Packing Technology into an iCon
Analogue and Digital Design
Embedded Software
Mechanics, Plastics and Glass
Micro-Machines (MEMs)
Displays and Transducers
Robotics and Test
Knowledge and Know-How
Research, Education and Training
Components, Sub-Systems and Systems;
Design, Assembly and Manufacture
Metrology, Methodology and Tools
... Involving Many Specialist Businesses
... Round and Round the World
...Not-Least from Europe
31

32. Architecting your Product


: Is the cumulative non-functional choices made to
support the functional need
 A Good Architecture is the one that ‘survives’
 History is written by winners (2nd is for losers)
: Component Performance may be ‘poor’ as long
as System Performance is ‘better’ for its use.
 Architectural Options ...
: Business Model (Cost-of Ownership, ROI), TTM (Productivity, History, IPAvailability, Know-How), Aesthetics (Power, Quality, Behaviour, Appearance)

: Analogue, Digital, Mechanical, Optical, RF, Software, Plastics,
Metal-forming, Manufacturing, Glass, ...

: More than 99% of a Product is Reused from its Predecessor

...
32
is assumed (working is expected!)
... It used to be the only consideration!

33. Power Philosophy
 Hardware Dissipates Power ...

Chose Underlying Technology for best power efficiency.

One size does not fit all (Products, Applications or Instances)
 ... Software Doesn’t (But it Tells Hardware To!)


Chips can literaly melt-down under software ‘instruction’
Make computing hardware power as ‘Activity’ dependent as possible


Zero Activity => Zero Power
Make OS/Apps aware of the power/performance situation,
and their options for controlling it (Need Indicators and Levers)
 ... Think System: It’s how the ‘box’ performs, not the components
33

34. Core Power Management
 For Processor and Peripheral Circuitry...
 Variable/Gated - Clock Domains
 Variable/Switched - Power Domains
 Indicators and Levers
 Allow the software to see and influence what is going on
 Principles of Core Power Efficiency...
 Minimise voltage/frequency (P=CV2f) so that processor has just



enough performance for the current application need
Maximises ‘Activity Power’ dependence (Zero Activity => Zero Power)
Management by the OS and the Application SW
Apply to all on & off-chip zones (not just the CPU) ...


34
Methodology
Retention Flops/Latches, Level Shifters, Power-Switch Cells, PLLs

35. Architectural Energy Efficiency - Parallelism
Processor
Input
Output
Output
Processor
f
Input
f/2
Processor
f
f/2
Capacitance = C
Voltage = V
Frequency = f
Capacitance = 2.2C
Voltage = 0.6V
Frequency = 0.5f
Power = CV2f
Power = (2.2*0.6*0.6*0.5)CV2f = 0.4CV2f
 To a limit determined by Amdahl’s or Gustafson’s Law ...
 Amdahl: Extracted parallelism from existing code (Reuse)
 Gustafson: Some needs only benefit from parallelism (Custom)
... Actual improvement is application specific.
35

36. Architectural Energy Efficiency - Data
 Moving Data takes significant Energy

Becoming the dominant energy consumption in a system
 Data Location
 Avoid moving or copying Data
 Energy ∝ DataVolume x Speed x Distance>2(3)
 Bring the processing to the data
 Bring the Processing to the Data
 Caching is good (depends on implementation)
 Write back is better than write-through
 Local working memory is good
 Aka Software Caching
... The Arrangement of your Data matters!
36

37. All ARM Processors are Power Efficient
37

38. Chose The Horses for The Course
About 50MTr
About 50KTr
... Delivering ~5x speed (Architecture + Process + Clock)
38

39. Multicore ARM On-Chip ...
 Heterogeneous Multicore Systems

have been in ARM for a long time:
Application
UI & 3D Graphics
Power Manager
Cortex™-A8
Mali™-400
MP
Cortex-M3
Interconnect
Memory
39

40. Coherent Multicore Cluster ...
 Homogenous Multicore

cluster, as part of a heterogeneous system:
Cortex-A9
Power Manager
Mali-400 MP
…
User Interface
and 3D graphics
Cortex-M3
Cortex-A9
Coherency Logic
Interconnect
40

41. Multiple Clusters ...
 Multiple Homogeneous Coherent Clusters
…
Cortex-A15
Cortex-A15
Coherency Logic in L2 Cache
…
Cortex-A15
Coherency Logic in L2 Cache
Coherent Interconnect
41
Cortex-A15

42. Computer On a Chip c2010 ...
Today’s Consumer require a pocket ‘Super-Computer’ ...
 Silicon Technology Provides a Billion transistors ...
 It will be supported with a few GB of memory ...
• Typically 10 Processors ...
•
•
•
•
•
•
42
http://www.arm.com/
4 x A9 Processors (2x2):
4 x MALI 400 Frag. Proc
1 x MALI 400 Vertex Proc
1 x MALI Video CoDec
Software Stacks, OS’s and Design
Tools/
ARM Technology gives
chip/system designers ...
• Improved Productivity
• Improved TTM
• Improved Quality/Certainty

43. CoreLink™ CCN-504 and DMC-520
Heterogeneous processors – CPU, GPU,
DSP and accelerators
Virtualized Interrupts
Up to 4 cores
per cluster
Up to 4
coherent
clusters
Quad
CortexA15
Quad
CortexA15
Quad
CortexA15
L2 cache
L2 cache
L2 cache
Quad
ACE
CortexA15
L2 cache
DSP
DSP
DSP
PCIe
DPI
Crypto
USB
AHB
ACE
SATA
NIC-400
IO Virtualisation with System MMU
CoreLink™ CCN-504 Cache Coherent Network
Integrated
L3 cache
Snoop
Filter
8-16MB L3 cache
CoreLink™
DMC-520
Dual channel
DDR3/4 x72
10-40
GbE
Interrupt Control
Uniform
System
memory
CoreLink™
DMC-520
NIC-400 Network Interconnect
PHY
x72
DDR4-3200
x72
DDR4-3200
Flash
GPIO
Peripheral address space
43
Up to 18
AMBA
interfaces for
I/O coherent
accelerators
and IO

44. Methodology As Well As Hardware
 C/C++
 Debug & Trace
Development
Energy Trace
Modules
 Middleware
44

45. big.LITTLE Processing
 For High-Performance systems...
 Tightly coupled combination of two ARM CPU clusters:


Cortex-A15 and Cortex-A7 - functionally identical
Same programmers view, looks the same to OS and applications
 big.LITTLE combines high-performance and low power


Automatically selects the right processor for the right job
Redefines the efficiency/performance trade-off
“Demanding tasks”
>2x Performance
Current big.LITTLE
smartphone
45
big
“Always on, always
connected tasks”
LITTLE
30% of the Power
(select use cases)
Current big.LITTLE
smartphone

46. LITTLE
Fine-Tuned to Different Performance Points
Most energy-efficient applications processor from ARM


Simple, in-order, 8 stage pipelines
Performance better than mainstream, high-volume
smartphones (Cortex-A8 and Cortex-A9)
big
Highest performance in mobile power envelope
46


Complex, out-of-order, multi-issue pipelines
Up to 2x the performance of today’s high-end
smartphones
Cortex-A7
Cortex-A53
Q
u
e
u
e
I
s
s
u
e
I
n
t
e
g
e
r
Cortex-A15
Cortex-A57

47. big.LITTLE Software
CPU Migration
 Migrate a single processor workload to the appropriate CPU
 Migration = save context then resume on another core
 Also known as Linaro “In Kernel Switcher”
 DVFS driver modifications and kernel modifications
 Based on standard power management routines
 Small modification to OS and DVFS, ~600 lines of code
big.LITTLE MP
 OS scheduler moves threads/tasks to appropriate CPU
 Based on CPU workload
 Based on dynamic thread performance requirements
 Enables highest peak performance by using all cores at once
47

48. Bringing the Processing to the Data …
Press Claims:
Dell + Marvell, Copper
BaiDu + Marvell, Baserock
 288 server nodes in a 4U rack space
Public Source: http://www.engadget.com/2011/11/02/hp-and-calxedas-moonshot-arm-servers-will-bring-all-the-boys-to/
48

49. ... Refining Data into Information
49

50. Transferrable Lessons to GP Software

 Moving data is Power Expensive ...
 Don’t move data; use it locally (Cache it)
 Refine it once, use it often (Pre-Process it)
 Your CPU Power is work-load independent ...
 So, get in; get the work done; and get out.
 Maximise the workload of your code; terminate when complete.
 Make your Processing work-load dependent
 Use a Hypervisor and turn off (at least free) processors not in use.
50

51. Societies Challenges in the 21c
 Urbanisation (Smart Cities)
 Health (eHealth)
 Transport
 Energy (Smart Grid)
 Security
 Environment
 Food/Water
 Ageing Society
 Sustainability
 Digital Inclusion
 Economics
And whilst our technologies will be an
essential part of all solutions, they
cannot not fix them without Society’s
help and cooperation!
... Energy Efficient Computing will minimise
the impact not avert the challenges!
51
Having a great time!

52. Conclusions
 Putting the power of Computation into the hands of the masses,
has changed the face of Computing (again)

Electronic Systems will become Essential to our Lives and the Economy
 Power Efficient ES are a major issue to Society

Which faces a future with them as a significant energy consumer in themselves
 Power Efficiency must be architected into the System Hardware
and Software from the beginning




52
To realise the maximum potential out of your Silicon (Avoiding Dark Si)
Architect & Design HW as efficiently as possible (reflecting the task)
 Strive for: No Work => No Power
Equip HW with Indicators and Levers so the System/App can manage it
Bring Processing to the Data ...
 Don’t move Data; move Information
 Process data Locally
 Energy ∝ DataVolume x Speed x Distance>2(3)

53. Computing at the heart of the 21c
ARM:
Enabling the Creation of
High-Performance Electronic Systems
--• Productively, Economically and Reliably
• Through Hw/Sw Reuse Methodologies
• Based on a family of CPU/GPU cores
53