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Enabling hard constraints in differentiable neural network and accelerator co-exploration
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- Authors
- Issue Date
- 2022
- Citation
- Proceedings - Design Automation Conference, pp.589-594
- 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.
- ISSN
- 0738-100X
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Related Researcher
- College of Engineering
- Department of Electrical and Computer Engineering
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