Selfcalibrating Online Wearout Detection

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Self-calibrating Online Wearout
Detection Authors Jason Blome Shuguang
Feng Shantanu Gupta Scott Mahlke
MICRO-40 December 3, 2007
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Motivation

• Designing Reliable Systems from Unreliable
  Components
• - Shekhar Borkar (Intel)

Failures will be wearout induced
More failures to come
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Current Approaches

• Traditional
• Design margins
• Burn-in
• Detection based on replication of computation
• TMR (Tandem/HP NonStop servers)
• DIVA (Bower, MICRO05)
• Prediction utilizes precise analytical models
  and/or sensors
• Canary circuits (SentinelSilicion, RidgeTop)
• RAMP (Srinivasan, UIUC/IBM)

Impractical
Static
Costly
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Wearout Mechanisms

• Many failure mechanisms have been shown to be
  progressive

• Hot carrier injection (HCI)

• Negative Bias Temperature Inversion (NBTI)

• Electromigration (EM)

• Oxide Breakdown (OBD)

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Objective

• Propose a failure prediction technique that
  exploits the progressive nature of wearout
• Monitor impact on path delays

• Prediction
• Monitors evolution of wearout
• Proactive
• enables failure avoidance/mitigation
• Continuous feedback
• False negatives and positives

• Detection
• Identifies existing fault
• Reactive
• enables failure recovery
• End-of-life feedback
• False negatives

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Oxide Breakdown (OBD)

• Accumulation of defects leads to a conductive
  path

Percolation Model Stathis, JAP06
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OBD HSPICE Model

• Post-breakdown leakage modeling

BSIM4.6.0, 06
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Characterization Testbench

• 90nm standard cell library

tcircuit
tcell
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Impact on Propagation Delay
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Delay Profiling Unit (DPU)
input signal
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Latency Sampling
uArch Module
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TRIX Analysis
Magnitude of divergence between TRIXglobal and
TRIXlocal reflects amount of degradation
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TRIX Analysis Details

• Exponential Moving Average (EMA)
• Triple-smoothed Exponential Moving Average

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Noisy Latency Profile
Percent Nominal Delay ()
Increasing Age
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DPU with TRIX Hardware
TRIXl Calculation
input signal
Latency Sampling
Prediction
TRIXg Calculation
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Wearout Detection Unit (WDU)
TRIXl Calculation
Latency Sampling
Prediction
TRIXg Calculation
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Evaluation Framework
Gate-level Processor Simulator
OR1200 Verilog
Synthesis and Place and Route
90nm Library
Timing, Power, and Temperature Simulations
MediaBench Suite
Workload Simulator
OBD Wearout Model
HSPICE Simulations
Wearout Simulator
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WDU Accuracy
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WDU Overhead
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WDU Overhead
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Long-term Vision

• Introspective Reliability Management (IRM)
• Intelligent reliability management directed by
  on-chip sensor feedback
• Prospective sensors
• Delay (WDU)
• Leakage/Vt
• Temperature

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Introspective Reliability Management
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Conclusions

• Many progressive wearout phenomenon impact
  device-level performance.
• Its possible to characterize this impact and
  anticipate failures
• WDU performance
• Failure predicted within 20 of end of life
  (tunable)
• Area overhead lt 3 (hybrid)
• Low-level sensors can be used to enable
  intelligent reliability management

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Questions?
?