80 citations · 142 across the 9 of their papers we have counts for
3 papers · 1 filter
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…
TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference
Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh +4
TensorDash is a hardware level technique for enabling data-parallel MAC units to take advantage of sparsity in their input operand streams. When used to compose a hardware accelera…