1 citations · 1 across the 1 of their papers we have counts for
4 papers
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference
Alberto Delmas Lascorz, Mostafa Mahmoud, Andreas Moshovos
Data accesses between on- and off-chip memories account for a large fraction of overall energy consumption during inference with deep learning networks. We present APack, a simple…
FPRaker: A Processing Element For Accelerating Neural Network Training
Omar Mohamed Awad, Mostafa Mahmoud, Isak Edo +5
We present FPRaker, a processing element for composing training accelerators. FPRaker processes several floating-point multiply-accumulation operations concurrently and accumulates…
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…
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…