84 citations · 173 across the 10 of their papers we have counts for
10 papers
Training CNNs faster with Dynamic Input and Kernel Downsampling
Zissis Poulos, Ali Nouri, Andreas Moshovos
We reduce training time in convolutional networks (CNNs) with a method that, for some of the mini-batches: a) scales down the resolution of input images via downsampling, and b) re…
Laconic Deep Learning Computing
Sayeh Sharify, Mostafa Mahmoud, Alberto Delmas Lascorz +2
We motivate a method for transparently identifying ineffectual computations in unmodified Deep Learning models and without affecting accuracy. Specifically, we show that if we deco…
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…
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…