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arXiv · 2301.09312

Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration

Deokki Hong·Kanghyun Choi·Hye Yoon Lee·Joonsang Yu·Noseong Park·Youngsok Kim·Jinho Lee
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Abstract

Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-profile systems. The large co-exploration space is often handled by adopting the idea of differentiable neural architecture search. However, despite the superior search efficiency of the differentiable co-exploration, it faces a critical challenge of not being able to systematically satisfy hard constraints such as frame rate. To handle the hard constraint problem of differentiable co-exploration, we propose HDX, which searches for hard-constrained solutions without compromising the global design objectives. By manipulating the gradients in the interest of the given hard constraint, high-quality solutions satisfying the constraint can be obtained.

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Deokki Hong, Kanghyun Choi, Hye Yoon Lee, Joonsang Yu, Noseong Park, Youngsok Kim, Jinho Lee. 2023-01-23. Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration. https://arxiv.org/abs/2301.09312

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