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Cerebras Systems (United States)

9 papers here279 citations across 9
fields
  • cs.DC4
  • cs.LG2
  • cs.AR1
  • cs.CL1
  • eess.IV1
ROR 040zz8080OpenAlex

affiliations via OpenAlex

output
20182025
most citedTensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference

80 citations

researchers with a paper here
  • I. Sharapov2 · h 13
  • Joel Hestness2 · h 18
  • A. Deiana1 · h 37
  • A. Gholami1 · h 44
  • Ali Hadi Zadeh1 · h 6
  • A. Moshovos1 · h 13
  • Andy Davis1 · h 9
  • Anne-Sophie Berthold1 · h 3
  • Babar Khan1 · h 4
  • B. von Krosigk1
  • C. Herwig1 · h 9
  • Daniele De Sensi1
collaborating institutions
  • Massachusetts Institute of TechnologyUS2 papers
  • Princeton UniversityUS2 papers
  • Birla Institute of Technology and Science, PilaniIN1 paper
  • Carnegie Mellon UniversityUS1 paper
  • Cerebral Diagnostics (Canada)CA1 paper
  • Columbia UniversityUS1 paper
  • Duke UniversityUS1 paper
  • ETH ZurichCH1 paper
  • European Organization for Nuclear ResearchCH1 paper
  • Fermi National Accelerator LaboratoryUS1 paper
  • Google DeepMind (United Kingdom)GB1 paper
  • Google (United States)US1 paper
Showing cs.ARShow all

1 paper · 1 filter

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…

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