33 citations · 91 across the 17 of their papers we have counts for
4 papers · 1 filter
Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability
Alberto Delmas, Sayeh Sharify, Patrick Judd +1
Tartan (TRT), a hardware accelerator for inference with Deep Neural Networks (DNNs), is presented and evaluated on Convolutional Neural Networks. TRT exploits the variable per laye…
Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks
Sayeh Sharify, Alberto Delmas Lascorz, Kevin Siu +2
Loom (LM), a hardware inference accelerator for Convolutional Neural Networks (CNNs) is presented. In LM every bit of data precision that can be saved translates to proportional pe…
Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks
Alberto Delmas, Patrick Judd, Sayeh Sharify +1
Stripes is a Deep Neural Network (DNN) accelerator that uses bit-serial computation to offer performance that is proportional to the fixed-point precision of the activation values.…
Cnvlutin2: Ineffectual-Activation-and-Weight-Free Deep Neural Network Computing
Patrick Judd, Alberto Delmas, Sayeh Sharify +1
We discuss several modifications and extensions over the previous proposed Cnvlutin (CNV) accelerator for convolutional and fully-connected layers of Deep Learning Network. We firs…