RESEARCH

SYSTEMS RESEARCH

Rethinking floating point for deep learning

December 07, 2018

Abstract

Reducing hardware overhead of neural networks for faster or lower power inference and training is an active area of research. Uniform quantization using integer multiply-add has been thoroughly investigated, which requires learning many quantization parameters, fine-tuning training or other prerequisites. Little effort is made to improve floating point relative to this baseline; it remains energy inefficient, and word size reduction yields drastic loss in needed dynamic range. We improve floating point to be more energy efficient than equivalent bit width integer hardware on a 28 nm ASIC process while retaining accuracy in 8 bits with a novel hybrid log multiply/linear add, Kulisch accumulation and tapered encodings from Gustafson’s posit format. With no network retraining, and drop-in replacement of all math and float32 parameters via round-to-nearest-even only, this open-sourced 8-bit log float is within 0.9% top-1 and 0.2% top-5 accuracy of the original float32 ResNet-50 CNN model on ImageNet. Unlike int8 quantization, it is still a general purpose floating point arithmetic, interpretable out-of-the-box. Our 8/38-bit log float multiply-add is synthesized and power profiled at 28 nm at 0.96× the power and 1.12× the area of 8/32-bit integer multiply-add. In 16 bits, our log float multiply-add is 0.59× the power and 0.68× the area of IEEE 754 float16 fused multiply-add, maintaining the same signficand precision and dynamic range, proving useful for training ASICs as well.

Download the Paper

AUTHORS

Written by

Jeff Johnson

Publisher

NIPS Systems for ML Workshop

Research Topics

Systems Research

Related Publications

November 20, 2024

SYSTEMS RESEARCH

FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision

Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao

November 20, 2024

July 23, 2024

SYSTEMS RESEARCH

CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models

Shengye Wan, Cyrus Nikolaidis, Daniel Song, David Molnar, James Crnkovich, Jayson Grace, Manish Bhatt, Sahana Chennabasappa, Spencer Whitman, Stephanie Ding, Vlad Ionescu, Yue Li, Joshua Saxe

July 23, 2024

June 27, 2024

SYSTEMS RESEARCH

Meta Large Language Model Compiler: Foundation Models of Compiler Optimization

Chris Cummins, Volker Seeker, Dejan Grubisic, Baptiste Rozière, Jonas Gehring, Gabriel Synnaeve, Hugh Leather

June 27, 2024

June 14, 2024

NLP

SYSTEMS RESEARCH

LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding

Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Bilge Acun, Ahmed Aly, Beidi Chen, Carole-Jean Wu, Ahmed Roman, Nas Mahmoud, Saurabh Agarwal

June 14, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.