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arXiv · 2203.15874

Temperature-Aware Monolithic 3D DNN Accelerators for Biomedical Applications

Prachi Shukla·Vasilis F. Pavlidis·Emre Salman·Ayse K. Coskun
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Abstract

In this paper, we focus on temperature-aware Monolithic 3D (Mono3D) deep neural network (DNN) inference accelerators for biomedical applications. We develop an optimizer that tunes aspect ratios and footprint of the accelerator under user-defined performance and thermal constraints, and generates near-optimal configurations. Using the proposed Mono3D optimizer, we demonstrate up to 61% improvement in energy efficiency for biomedical applications over a performance-optimized accelerator.

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Prachi Shukla, Vasilis F. Pavlidis, Emre Salman, Ayse K. Coskun. 2022-03-29. Temperature-Aware Monolithic 3D DNN Accelerators for Biomedical Applications. https://arxiv.org/abs/2203.15874

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