Customized accelerators for Convolutional Neural Network (CNN) can achieve better energy efficiency than general computing platforms. However, the design of a high-performance accelerator should take into account a variety of parameters and physical constraints. The increasing parameters and tighter constraints gradually complicate the design space, which poses new challenges to the capacity and efficiency of design space exploration methods. In this paper, we provide a novel design space exploration method named ACDSE for optimizing the design process of CNN accelerators. ACDSE implements the adaptive compression mechanism to dynamically adjust the search range and prune low-value design points according to the exploration states. As a result, it can focus on valuable subspace while also improving exploration capacity and efficiency. Additionally, we implement ACDSE to address the problem of CNN accelerator latency optimization. The experiment indicates that, compared to former DSE methods, ACDSE can reduce latency and increase efficiency by 1.39x-5.07x and 2.07x-43.87x, respectively, under the most stringent constraint conditions, demonstrating its superior adaptability to the complicated design space.

为卷积神经网络（CNN）定制的加速器可以实现比通用计算平台更好的能源效率。然而，高性能加速器的设计应考虑各种参数和物理约束。不断增加的参数和更严格的约束使设计空间逐渐复杂化，这对设计空间探索方法的容量和效率提出了新的挑战。在本文中，我们提供了一种名为 ACDSE 的新型设计空间探索方法，用于优化 CNN 加速器的设计过程。ACDSE实现了自适应压缩机制，可以根据探索状态动态调整搜索范围并修剪低值设计点。因此，它可以专注于有价值的子空间，同时提高探索能力和效率。此外，我们还实现了 ACDSE 来解决 CNN 加速器延迟优化的问题。实验表明，与之前的DSE方法相比，在最严格的约束条件下，ACDSE可以将延迟降低1.39x-5.07x，效率提高2.07x-43.87x，展示了其对复杂设计空间的卓越适应性。