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- W4293025002 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." @default.
- W4293025002 created "2022-08-25" @default.
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- W4293025002 date "2022-07-10" @default.
- W4293025002 modified "2023-10-02" @default.
- W4293025002 title "Enabling hard constraints in differentiable neural network and accelerator co-exploration" @default.
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- W4293025002 doi "https://doi.org/10.1145/3489517.3530507" @default.
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