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.

翻译：共同探索最佳神经结构及其硬件加速器是一种越来越受关注的方法,它解决了计算成本问题,特别是在低调系统中。大型共同探索空间往往通过采用不同神经结构搜索的概念来处理。然而,尽管不同的共同探索具有较高的搜索效率,但它面临着一个严峻的挑战,即无法系统地满足框架率等硬性制约。为了处理不同共试的硬性制约问题,我们建议HDX,在不损害全球设计目标的情况下,寻找难以控制的解决办法。通过操纵梯度来满足这一制约,就可以获得满足这一制约的高质量解决方案。