MicroQonv: Reshaping Convolution Tensors for Efficient Microscaling in Training and Inference
Microscaling quantization techniques are increasingly used to represent neural network parameters with 8 bits or fewer while preserving near-full precision accuracy. However, applying these methods efficiently in convolutional layers is not straightforward. A naive approach transfers full-precision weights and activations to processing units and quantizes each tensor twice, resulting in much more memory movement than expected. Additional overhead comes from the activation tensors, whose sizes grow substantially because of the im2col transformation applied before quantization. We propose MicroQonv, a way to combine microscaling with convolutional layers' forward and backward operations by ...
| Authors | Romain Facq, Sami Ben Ali, Olivier Sentieys |
|---|---|
| Primary category | cs.AR |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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