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A Framework for Dynamic Energy Efficiency and Temperature Management (DEETM) Michael Huang, Jose Renau, Seung-Moon Yoo, Josep Torrellas University of Illinois.

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Presentation on theme: "A Framework for Dynamic Energy Efficiency and Temperature Management (DEETM) Michael Huang, Jose Renau, Seung-Moon Yoo, Josep Torrellas University of Illinois."— Presentation transcript:

1 A Framework for Dynamic Energy Efficiency and Temperature Management (DEETM) Michael Huang, Jose Renau, Seung-Moon Yoo, Josep Torrellas University of Illinois at Urbana-Champaign

2 Micro’33, Dec Motivation  Increasingly high power consumption –High temperature –Inefficient use of energy  Limitations of existing approaches –Static optimizations –Coarse grain dynamic management (DPM) –Inefficient temperature control (sleep) –Independent targets: temp, energy efficiency

3 Micro’33, Dec Goal  Temperature: enforce limit while minimizing slowdown  Energy efficiency: maximize energy saving while exploiting performance slack Unified framework for Dynamic Energy Efficiency and Temperature Management (DEETM)

4 Micro’33, Dec Contribution: DTEEM Framework  Existing limitations: –static application –coarse grain dynamic –inefficient techniques –independent targets:  temperature control  energy efficiency  DEETM approach: –multiple techniques –dynamic –fine grain –order techniques for maximum efficiency –unified target

5 Micro’33, Dec DEETM Framework  Monitors temperature & execution slack  Runs decision algorithm periodically: {Thermal, Slack} components  Activates low-power techniques –dynamically –incrementally –in prioritized order

6 Micro’33, Dec Techniques  Instruction filter cache  Data cache subbanking  Voltage scaling  Voltage scaling: DRAM only  Light sleep

7 Micro’33, Dec Instruction Filter Cache Filter Cache Instruction Memory Processor Filter Cache L1 Cache Processor High power mode: Time: 1 Energy: E Filter Cache Instruction Memory Processor High power mode: Time: 1 Energy: E Filter Cache Instruction Memory Processor Low power mode: Time: 1 + mr*1 Energy: e + mr*E

8 Micro’33, Dec Techniques  Instruction filter cache  Data cache subbanking  Voltage scaling  Voltage scaling: DRAM only  Light sleep

9 Micro’33, Dec Chip Environment  Processor-in-memory  64 lean processors –2-issue static  Optimized memory hierarchy  Integrated thermal sensors and instruction counter Processor D-Cache I-Cache DRAM Bank Instruction Memory Processor DRAM Bank Interconnect

10 Micro’33, Dec Individual Techniques-E*D IFilter SubBank VoltFreq 0.7 Normalized Energy-Delay Product Normalized Average Power MemVolt Sleep

11 Micro’33, Dec Combinations - E*D Normalized Average Power Normalized Energy-Delay Product VoltFreq IFilter Gradual

12 Micro’33, Dec Summary  Techniques ordered by efficiency (same order apply to both targets)  System applies techniques in order, dynamically and incrementally

13 Micro’33, Dec Thermal Algorithm Macrocycle

14 Micro’33, Dec Temperature Control - Limit

15 Micro’33, Dec Temperature Control - Slowdown

16 Micro’33, Dec Slack Algorithm Macrocycle Effective IPC*: frequency-adjusted Microcycle Every Macrocycle (HW/OS) The First Few Microcycles (HW)

17 Micro’33, Dec Slack - Energy Consumption

18 Micro’33, Dec Slack Misprediction

19 Micro’33, Dec Related Issues  Algorithm interaction  Selecting macrocycle  Handling Thermal Crisis situation  Reducing technique activation overhead  Flexible technique ordering  Hardware vs. software implementation

20 Micro’33, Dec Conclusions  Effective & efficient temperature control –very few macrocycles still over limit –27% longer execution vs. 98% (by sleeping)  Efficient & accurate fine-grain exploitation of execution slack –5% slack  27% energy saved –small slack misprediction


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