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cs.AR2026
MicroQonv: Reshaping Convolution Tensors for Efficient Microscaling in Training and Inference
Romain Facq, Sami Ben Ali, Olivier Sentieys
Microscaling quantization techniques are increasingly used to represent neural network parameters with 8 bits or fewer while preserving near-full precision accuracy. However, apply…
cs.AR2024
A Stochastic Rounding-Enabled Low-Precision Floating-Point MAC for DNN Training
Sami Ben Ali, Silviu-Ioan Filip, Olivier Sentieys
Training Deep Neural Networks (DNNs) can be computationally demanding, particularly when dealing with large models. Recent work has aimed to mitigate this computational challenge b…