research
∙
09/12/2022
FP8 Formats for Deep Learning
FP8 is a natural progression for accelerating deep learning training inf...
share
research
∙
04/20/2020
Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation
Quantization techniques can reduce the size of Deep Neural Networks and ...
research
∙
04/17/2018
DPRed: Making Typical Activation Values Matter In Deep Learning Computing
We show that selecting a fixed precision for all activations in Convolut...
research
∙
03/09/2018
Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How
We show that, during inference with Convolutional Neural Networks (CNNs)...
research
∙
07/27/2017
Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability
Tartan (TRT), a hardware accelerator for inference with Deep Neural Netw...
research
∙
06/23/2017
Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks
Loom (LM), a hardware inference accelerator for Convolutional Neural Net...
research
∙
06/01/2017
Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks
Stripes is a Deep Neural Network (DNN) accelerator that uses bit-serial ...
research
∙
11/17/2015