FPRaker: A Processing Element For Accelerating Neural Network Training

no code implementations • 15 Oct 2020 • Omar Mohamed Awad, Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh, Ciaran Bannon, Anand Jayarajan, Gennady Pekhimenko, Andreas Moshovos

We demonstrate that FPRaker can be used to compose an accelerator for training and that it can improve performance and energy efficiency compared to using conventional floating-point units under ISO-compute area constraints.

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TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference

no code implementations • 1 Sep 2020 • Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh, Omar Mohamed Awad, Gennady Pekhimenko, Jorge Albericio, Andreas Moshovos

TensorDash is a hardware level technique for enabling data-parallel MAC units to take advantage of sparsity in their input operand streams.

GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference

no code implementations • 8 May 2020 • Ali Hadi Zadeh, Isak Edo, Omar Mohamed Awad, Andreas Moshovos

Second, we present a co-designed hardware architecture that also reduces computation.

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