Hadamard-domain write-and-verify (HD-PV and HARP) reduces uncorrelated read-noise variance by a factor of N in RRAM, preserving ML accuracy under severe noise with up to 6.1x lower latency and 9.5x better energy efficiency than conventional methods.
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TrilinearCIM enables complete in-memory Transformer attention computation via DG-FeFET three-operand MAC without runtime NVM reprogramming, delivering up to 46.6% energy reduction and 20.4% latency improvement on BERT and ViT benchmarks at 37.3% area cost.
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HARP: Hadamard-Domain Write-and-Verify for Noise-Robust RRAM Programming
Hadamard-domain write-and-verify (HD-PV and HARP) reduces uncorrelated read-noise variance by a factor of N in RRAM, preserving ML accuracy under severe noise with up to 6.1x lower latency and 9.5x better energy efficiency than conventional methods.
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Trilinear Compute-in-Memory Architecture for Energy-Efficient Transformer Acceleration
TrilinearCIM enables complete in-memory Transformer attention computation via DG-FeFET three-operand MAC without runtime NVM reprogramming, delivering up to 46.6% energy reduction and 20.4% latency improvement on BERT and ViT benchmarks at 37.3% area cost.