Lecture 14 Software Design for Low-Power

1
Lecture 14Software Design for Low-Power

• Software dictates much of hardware activity
• Need software power estimation method
• Must optimize software at several levels of
  abstraction
• Ultimately involves hardware/software trade-offs
• Summary

• Michael L. Bushnell
• CAIP Center and WINLAB
• ECE Dept., Rutgers U., Piscataway, NJ

2
Sources of Software Power Dissipation

• Memory system uses power (1/10 to ¼) in portable
  computers
• Dominates in video processing
• System bus switching activity controlled by
  software
• ALU and FPU data paths needs good scheduling to
  avoid pipeline stalls
• Control logic and clock reduce by using
  shortest possible program to do the computation
• Software control of hardware
• Reduces power of idle components
• Controls power saving modes

3
Memory Accesses Are Expensive

• Highly capacitive data / address / row / column
  decode / word data lines with high fanout
• Mapping of data structures to memory banks
  determines possibility of parallel word loads
  (which are more energy efficient)
• Memory access patterns greatly affect cache
  performance
• Cache more power efficient than main memory
• Closer to CPU and smaller than main memory

4
Methods of Software Power Estimation for Code

• Lower level use gate-level simulation power
  estimation on gate-level description of hardware
  running the code most accurate
• Higher level look at frequency of each type of
  instruction / sequence (execution profile)
• Use lookup table
• Model ALUs, register files, etc.
• Use bus switching activity exclusively
• Must know bus architecture, OPCODEs,
  representative input data to program, mapping of
  code/data to address space
• Characterize instruction power empirically on
  real hardware

5
Instruction Level Power Analysis

• For each instruction, need to measure
• Base power cost (independent of prior state)
• Prior state cost needs large-scale analysis /
  simulation
• Pipeline stalls, buffer stalls, cache misses
• Circuit state effects due to localized
  processor state change from execution of
  instruction pair
• Example ADD A B, C
• MULT C A, B
• Includes change of instruction bus state
• Switching of control lines
• ALU mode changes
• Routing costs to / from register file

6
Instruction Analysis Example

• EP overall energy cost of program
• Bi base cost of instruction type i
• Oi,j cost of instruction type i followed by
  type j
• Oi,j Oj,i
• Ek costs of pipeline stalls and cache misses

7
Detailed Example

• Simple DSP with 4 registers A, B, C, D
• Evaluate (x y) z

8
Example Concluded
9
Software Power Optimizations

• Select least expensive instructions / instruction
  sequences
• Minimize frequency or cost of memory access
• Use hardware power minimization features
• Algorithm choices
• Must map well to available hardware
• Be efficient for problem being solved
• Must maximize performance parallel processing
• Constraints
• Battery-powered system minimize total energy
  dissipated
• When heat dissipation or reliability is important
  minimize instantaneous / average power

10
Algorithm Transformations

• Reduction operations use parallel hardware, but
  run slowly

11
Two Adders Available
12
Four Adders Available
13
Minimizing Memory Access Costs

• Memory causes power and performance bottleneck
• Minimize accesses needed by algorithm
• Minimize total memory size needed by algorithm
• Put memory accesses as close as possible to CPU
• Register, then cache, external RAM last
• Efficiently use memory bandwidth
• Use multiple-word parallel loads, not single word
  loads

14
Improve Loop Nesting and Operation Order

• B too large to store in registers - used memory
  transfers, instead
• Loop rearrangement allowed intermediate B to stay
  in general register
• Got rid of 2N memory accesses for B and N memory
  locations not needed

15
Reduce Space Requirements

• C not needed afterward reorder so that B can
  overwrite C

16
Reduced Intermediate Value Storage

• Reduce storage for intermediate values AiM
• Reduced from N locations to 1

17
Dual Memory Load Example
18
Maximize Parallel Loads of Multiple Memory Words

• Dual word loads reduce energy by 47
• 1st maximize dual loads with memory allocation
• 2nd Combine memory accesses
• No dual load Dual
  load Dual load parallel exec.

19
Minimize Memory Bandwidth

• Allocate registers to minimize memory references
• Cache blocking loop unrolling fix array
  computations so that blocks of array only read
  once
• Register blocking eliminate redundant register
  loads
• Recurrence detection optimization use
  registers for values carried over from one
  recursion level to next
• Compact multiple memory references into 1
  reference
• 40 speedup on DEC alpha, 25 on Motorola 88100

20
Cache Locking

• Lock data into cache
• Prevents memory reads/writes from going to main
  memory
• Real benefit -- cache write hits not written
  through to main memory
• Read hits no main memory reference even if
  cache were unlocked
• Fujitsu SPARClite writing 0 drew 341 mA from
  power supply when cache unlocked, only 194 mA
  when cache locked

21
Instruction Selection/Ordering

• Instruction packing
• Single instruction does both ALU operation and
  memory data transfer
• Much instruction overhead not duplicated when
  operations run in parallel
• Concurrent execution of integer floating point
  Ops
• Easier to do in VLIW and superscalar
  architectures
• Reorder instructions to minimize circuit state
  effects
• Most significant for DSP units
• Accumulator spilling and mode switching are most
  sensitive

22
Operand Swapping/Ordering

• Swapping minimizes switching of functional unit
  inputs
• Example x 7 and y 7 keep 7 on same adder
  input
• Ordering most significant if commutative operands
  not treated symmetrically by hardware
• Example Booth multiplier
• 2nd operand bit pattern determines additions
  and subtractions (called recoding weight)
• Put operand with lowest recoding weight on 2nd
  operand
• Saved 10-30 of the power in Lees experiments

23
Power Management

• Software can often control processor power-down
  modes
• User interfaces activity comes in bursts
• When system idle time exceeds threshold, likely
  to continue to be idle start shutdown
• Example SPARClite
• Power-down register masks/enables clock for
• SDRAM, DMA module, FPU, floating-point queues
• Example Hitachi SH3
• Standby mode CPU core stopped, peripheral
  controller, bus controller, memory refresh
  continue
• Sleep mode Everything but real-time clock stops

24
More Examples

• Example Intel 486SL
• System Management Mode entered by asynchronous
  interrupt
• Can enable, disable, switch between fast and
  slow clocks for CPU and ISA bus
• Example PowerPC 603 and 604
• Dynamic power management removes clock from
  execution units (saves 8-16)
• Static power management
• Doze shuts off most function units, keeps bus
  snooping enabled to maintain data cache coherence
• Nap shuts off bus snooping and sets wakeup
  timer, keeps phase-locked loop running to allow
  quick clock restart
• Sleep mode also shuts off phase-locked loop
• Software control of power management better than
  pure hardware control more information

25
Automated Low-Power Code Generation

• High-level
• Graphical / textual languages available to
  describe DSP algorithms
• HYPER_LP uncovers parallelism and minimizes
  critical paths
• Allows data path supply voltage to be reduced
• MASAI reorganizes loops to minimize memory
  transfers and size
• DSP Compiler technology must deal with small
  register set, irregular data paths, fully using
  parallel resources

26
Sus Cold Scheduling Algorithm

• Allocate registers
• Pre-assemble Calculate target addresses, index
  symbol table, transform instructions to binary
• Schedule instructions with cold scheduling
• Post-assemble Complete assembly
• Reduced switching activity 20-30, performance
  loss of 2-4
• Lee et al. had a similar approach, but used an as
  soon as possible packing of instructions
• Saved 26 to 73 energy compared with no
  instruction packing and no memory bank assignment
  optimization

27
Instruction Set Co-Design

• PEAS-I system
• Takes HDL for CPU and C compiler, assembler, and
  simulator for CPU
• Takes design constraints (chip area, power),
  hardware module database, sample application
  program, program data set
• Optimizes instruction set and implementations for
  application program and given data set
• Starts with core instructions needed for any C
  program
• Augments with instructions for C operators not
  already included as a single instruction
• Defines hardware, microprogram, and software
  implementations
• Estimates power and area of each instruction
  implementation
• Accounts for pipeline hazards
• Solves as an integer program using
  branch-and-bound search

28
Instruction Set Design

• Huang and Despain system
• Optimizes instruction set for sample application,
  too
• Groups micro-operations (MOPS) together to form
  higher-level instructions
• Merge MOPS together as byproduct of scheduling
• MOPS must be scheduled to same clock cycle
• Constrain with instruction bit width, instruction
  set size, and hardware resources
• Solved by simulated annealing with objective of
  minimizing execution cycles instruction set size

29
Reconfigurable Computing

• Some of hardware interconnect and logic is
  modified at run time
• Can closely optimize to wide variety of
  applications
• Can reconfigure at gate level or at architectural
  level
• Usually implemented using FPGAs
• Gives software designer chance to tailor software
  and processor to fit each other
• Even after hardware design is fixed

30
Memory System Considerations

• Feature
• Total size
• Bank partitioning
• Wide data bus
• Proximity to CPU
• Cache size
• Cache protocol
• Cache locking

Low-Power Impact Code compaction, algorithm
transformations Smaller, lower C,
faster Determined by application size, data
structure Needed for parallel loads Reduces C,
makes memory use less costly Minimize cache
misses for application Need spatial and temporal
locality Optimize for application Use to run part
of application only from cache
31
Architectural Considerations

• Processor class DSP/RISC/CISC
• Parallel Processing VLIW, Superscalar, SIMD, MIMD
• Bus architecture
• Register file size

Application dependent Does parallel processing
greatly improve performance so that reduced VDD
and slower clock can be used? Separate address,
data, instruction, I/O busses make it easier to
optimize instructions, addressing, and data to
minimize bus activity Eases register allocation,
reduces memory accesses, too many increases power
32
Power Management Considerations

• Software vs. hardware control
• Granularity of clock/voltage removal
• Clock/voltage reduction
• Guarded evaluation

Software control lets power management be
tailored to application Shutdown modes needed to
benefit from minimized execution times Useful if
there is schedule slack for a software task
(power reduced during slack) Latch functional
unit inputs to avoid meaningless calculation when
not in use
33
Summary

• Estimation of software contribution to power
  dissipation
• Minimize software power dissipation
• Choose best algorithm that is suited to hardware
  resources
• Minimize memory size and expensive memory
  accesses
• Algorithm transformations
• Efficient data mapping onto memory
• Optimal use of memory bandwidth, registers
  cache
• Optimally use available parallelism for
  application
• Use hardware power management support
• Select instruction sequences to minimize
  switching in CPU and data path