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20162022
most citedTensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference

80 citations · 136 across the 7 of their papers we have counts for

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Showing 2020Show all

2 papers · 1 filter

cs.AR2020

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…

cs.AR2020★ 80 cited

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…