33 citations · 91 across the 17 of their papers we have counts for
6 papers · 1 filter
Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic Programming
Zihao Deng, Sayeh Sharify, Xin Wang +1
Quantization is a widely used technique to compress neural networks. Assigning uniform bit-widths across all layers can result in significant accuracy degradation at low precision…
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…
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.…