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Found 4 papers, 1 papers with code
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Data clipping is crucial in reducing noise in quantization operations and improving the achievable accuracy of quantization-aware training (QAT).
LNS-Madam: Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update
Representing deep neural networks (DNNs) in low-precision is a promising approach to enable efficient acceleration and memory reduction.
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
4-bit weights and 8-bit activations achieve near-full-precision accuracy for both BERT-base and BERT-large on SQuAD while reducing area by 26% compared to an 8-bit baseline.
Analog/Mixed-Signal Hardware Error Modeling for Deep Learning Inference
We also introduce an energy model to predict the requirements of high-accuracy AMS hardware running large networks and use it to show that for ADC-dominated designs, there is a direct tradeoff between energy efficiency and network accuracy.
