84 citations · 173 across the 10 of their papers we have counts for
3 papers · 1 filter
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…