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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NeuralLVC achieves better lossless compression than H.264 and H.265 on video sequences by combining masked diffusion with temporal conditioning on frame differences.
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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.
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NeuralLVC: Neural Lossless Video Compression via Masked Diffusion with Temporal Conditioning
NeuralLVC achieves better lossless compression than H.264 and H.265 on video sequences by combining masked diffusion with temporal conditioning on frame differences.