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cs.AR2026
MX-SAFE: Versatile Inference- and Training-Proof Microscaling Format with On-the-Fly Exponent and Mantissa Bit Allocation
Dahoon Park, Jahyun Koo, Sangwoo Hwang +1
As the demand for deep learning grows, cost reduction through quantization has become essential for both training and inference. In 2022, the Open Compute Project (OCP) consortium…
cs.AR2022
LightNorm: Area and Energy-Efficient Batch Normalization Hardware for On-Device DNN Training
Seock-Hwan Noh, Junsang Park, Dahoon Park +3
When training early-stage deep neural networks (DNNs), generating intermediate features via convolution or linear layers occupied most of the execution time. Accordingly, extensive…