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Rollback-Free Value Prediction with Approximate Loads

Rollback-Free Value Prediction with Approximate Loads. Georgia Institute of Technology Carnegie Mellon University. Mitigating Memory Wall with Approximation. Rollback-Free Value Prediction Microarchitecturally - triggered approximation

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Rollback-Free Value Prediction with Approximate Loads

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  1. Rollback-Free Value Predictionwith Approximate Loads Georgia Institute of Technology Carnegie Mellon University

  2. Mitigating Memory Wall with Approximation Rollback-Free Value Prediction • Microarchitecturally-triggered approximation • Predict the value of an approximate load when it misses in the cache • Do not checkfor mispredictions • Do not rollbackfrom mispredictions • Mitigate long latency memory accesses

  3. Rollback Free Value Prediction

  4. Design Principles Maximizeopportunities for performance and energy benefits Minimize the adverse effects of approximation on quality degradation

  5. Design Challenges and Solutions Target Performance-Critical Safe Loads • Profile-directed compilation • Usually, < 32 loads cause 80% of cache misses Utilize Fast-Learning Predictors • Two-delta stride predictor • Prediction: table lookup plus an addition Integrate RFVP with existing architecture

  6. Experimental Results with a Modern OoO Processor 1.8% 19% 0.8% 8% Average Average Maximum Maximum More CPU configurations and value predictors are in the paper

  7. Ongoing Work Mitigate both Memory Wall and Bandwidth Wall • Extend rollback-free value prediction to GPUs • Drop a fraction of the missed requests • Preliminary results: Up to 2x improvement in energy and performance with only 10% quality degradation

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