80 citations · 80 across the 2 of their papers we have counts for
3 papers
cs.AR2020★ 80 cited
TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference
Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh +4
TensorDash is a hardware level technique for enabling data-parallel MAC units to take advantage of sparsity in their input operand streams. When used to compose a hardware accelera…
cs.LG2016
Bit-pragmatic Deep Neural Network Computing
J. Albericio, P. Judd, A. Delmás +2
We quantify a source of ineffectual computations when processing the multiplications of the convolutional layers in Deep Neural Networks (DNNs) and propose Pragmatic (PRA), an arch…
cs.LG2015
Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets
Patrick Judd, Jorge Albericio, Tayler Hetherington +4
This work investigates how using reduced precision data in Convolutional Neural Networks (CNNs) affects network accuracy during classification. More specifically, this study consid…