80 citations · 136 across the 7 of their papers we have counts for
3 papers · 1 filter
Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training
Miloš Nikolić, Enrique Torres Sanchez, Jiahui Wang +5
The transfer of tensors from/to memory during neural network training dominates time and energy. To improve energy efficiency and performance, research has been exploring ways to u…
Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Models
Ali Hadi Zadeh, Mostafa Mahmoud, Ameer Abdelhadi +1
Increasingly larger and better Transformer models keep advancing state-of-the-art accuracy and capability for Natural Language Processing applications. These models demand more com…
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference
Alberto Delmas Lascorz, Mostafa Mahmoud, Andreas Moshovos
Data accesses between on- and off-chip memories account for a large fraction of overall energy consumption during inference with deep learning networks. We present APack, a simple…