18 citations · 54 across the 5 of their papers we have counts for
5 papers
DPRed: Making Typical Activation and Weight Values Matter In Deep Learning Computing
Alberto Delmas, Sayeh Sharify, Patrick Judd +3
We show that selecting a single data type (precision) for all values in Deep Neural Networks, even if that data type is different per layer, amounts to worst case design. Much shor…
Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How
Alberto Delmas, Patrick Judd, Dylan Malone Stuart +5
We show that, during inference with Convolutional Neural Networks (CNNs), more than 2x to $8x ineffectual work can be exposed if instead of targeting those weights and activations…
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…
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.…
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…