arXiv Machine Learning
Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters
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AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization
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NANQ: Noise-Floor-Aware Mixed-Precision Non-Uniform Quantization for Analog Compute-in-Memory
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TACO: Ternary Absolute-max Column-wise One-sparse Optimizer for LLM Fine-Tuning
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arXiv Machine Learning
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OpenACMv2: An Accuracy-Constrained Co-Optimization Framework for Approximate DCiM
arXiv:2603. 13042v2 Announce Type: replace Abstract: Digital Compute-in-Memory (DCiM) accelerates neural networks by reducing data movement.
arXiv Machine Learning
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WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
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arXiv AI
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MX-SAFE: Versatile Inference- and Training-Proof Microscaling Format with On-the-Fly Exponent and Mantissa Bit Allocation
arXiv:2605. 24391v2 Announce Type: replace-cross Abstract: As the demand for deep learning grows, cost reduction through quantization has become essential for both training and inference.