eec workshop 2014
TRANSCRIPT
Francesc [email protected]
http://www.linkedin.com/in/francescmoll
Autonomous (“Battery-less”) computingProspects and challenges
Outline
Energy harvesting management considerations
Computing energy considerations
Conclusions
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Energy/Power income profile
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Energy income
Energy reservoir
Power & Energy
Time
Energy
P_in
P_task1, P_task2, P_task3, P_task4, P_task5...
Conditions for autonomous operation:
• avg(P_in)=(>)avg(P_spent)
• E_rsvr =(>) E_spent while P_in < P_spent
Some kind of energy reservoir mandatory if any task
needs to be executed in between charging events
Energy-aware task manager of generic system
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System
Sensor acq.
Processing
ActuationCommu-nication
Display
Task manager must prioritize
tasks in function of remaining
energy and learned patterns of
energy income
Goal: reduce energy of each
task to decrease reservoir
requirements/increase time
between charges
Physical constraints in energy
Some subsystems energy are limited by physical
constraints, not technology
– may dominate total energy in some applications:
– Actuation
Weight, speed of movement
– Communications
Distance, noise, standards
Other subsystems energy depend on technology
– Display
– Computing
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Computing layers
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Device/Technology
Circuit
Architecture
Algorithm
• Parasitic capacitance
• Leakage
• VDD – noise margin
• Number of devices per gate
• Gate/Memory topology
• Parallelism
• Redundancy
• Number of transactions
Impact on Energy
Why don’t we see orders of magnitude improvement in energy efficiency?
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Device/Technology
Circuit
Architecture
Algorithm
Interaction between layers
New devices closer to
Landauer’s limit
Increase VDD/SNR
Increase devices per
gate (differential)
Increase redundancy
More complex
organization
Susceptibility to noise
Why don’t we see orders of magnitude improvement in energy efficiency?
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Device/Technology
Circuit
Architecture
Algorithm
Interaction between layers
Aggressive VDD
scaling
Loss of performance
Increase parallelism
Leakage & variability
Reduced noise margin
Increase redundancy
ECC
Body Bias islands
Why don’t we see orders of magnitude improvement in energy efficiency?
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Device/Technology
Circuit
Architecture
Algorithm
Interaction between layers
Adaptive Multi-
VDD
(Bi-directional) level
shifters
Challenging timing
closure
On-chip regulators
ECC
Complex task
scheduling
Some overheads may be cut by tolerance to noise and uncertainty
New circuit techniques:
– Averaging cells
Stochastic resonance
– Noise increases reliability in particular kinds of systems
Research needed to pursue this avenue
– How to build useful computing paradigm
– Actual energy footprint
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Concept: Suprathreshold Stochastic Resonance
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STOCHASTIC RESONANCE: PARTICULAR NOISE LEVELS ENHANCE THE BEHAVIOR OF A SIGNAL PROCESSING SYSTEM
Mark D. McDonnell and Nigel Stocks (2009)
Adaptive Averaging Cell (AD-AVG)
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degradation
noise
Variability-aware architectures based on hardware redundancy for nanoscale reliable
computation, Aymerich Capdevila, Nivard, PhD thesis, UPC, 2013
Degradation Stochastic Resonance (DSR)
Time degradation combined with noise present beneficial impact on AD-AVG systems
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σs : monitor noise
σmax (0.1344V): maximum
admissible variability level
Examples in nature show complex (specialized) systems at low energy
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There must be another way to make computation....
.... at the cost of flexibility and general application
Conclusions
Energy harvesting management at the system level
is still a challenge
Improvements in one layer are partially counteracted
by adjustments in other layers
– Look for tolerant systems
Extremely difficult to change computing paradigm for
general computing devices
Look for specialized computing devices where gains
can be much larger
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