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

Autor:	Hong, Deokki, Choi, Kanghyun, Lee, Hye Yoon, Yu, Joonsang, Park, Noseong, Kim, Youngsok, Lee, Jinho
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Hardware Architecture
Druh dokumentu:	Working Paper
Popis:	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. Comment: publisehd at DAC'22
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.09312 Zobrazit plný text záznamu View this record from Arxiv