Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training

no code implementations • 28 Apr 2022 • Miloš Nikolić, Enrique Torres Sanchez, Jiahui Wang, Ali Hadi Zadeh, Mostafa Mahmoud, Ameer Abdelhadi, Kareem Ibrahim, Andreas Moshovos

Quantum Mantissa and Quantum Exponent are machine learning compression methods that tap into the gradient descent algorithm to learn the minimal mantissa and exponent bitlengths on a per-layer granularity.

Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Models

no code implementations • 23 Mar 2022 • Ali Hadi Zadeh, Mostafa Mahmoud, Ameer Abdelhadi, Andreas Moshovos

Mokey reduces the footprint of state-of-the-art 32-bit or 16-bit floating-point transformer models by quantizing all values to 4-bit indexes into dictionaries of representative 16-bit fixed-point centroids.

Quantization