We propose a novel hardware and software co-exploration framework for efficient neural architecture search (NAS). Different from existing hardware-aware NAS which assumes a fixed hardware design and explores the neural architecture search space only, our framework simultaneously explores both the architecture search space and the hardware design space to identify the best neural architecture and hardware pairs that maximize both test accuracy and hardware efficiency. Such a practice greatly opens up the design freedom and pushes forward the Pareto frontier between hardware efficiency and test accuracy for better design tradeoffs. The framework iteratively performs a two-level (fast and slow) exploration. Without lengthy training, the fast exploration can effectively fine-tune hyperparameters and prune inferior architectures in terms of hardware specifications, which significantly accelerates the NAS process. Then, the slow exploration trains candidates on a validation set and updates a controller using the reinforcement learning to maximize the expected accuracy together with the hardware efficiency. Experiments on ImageNet show that our co-exploration NAS can find the neural architectures and associated hardware design with the same accuracy, 35.24% higher throughput, 54.05% higher energy efficiency and 136x reduced search time, compared with the state-of-the-art hardware-aware NAS.

中文翻译：

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神经架构的软硬件协同探索

我们提出了一种新颖的硬件和软件协同探索框架，用于高效的神经架构搜索（NAS）。与现有的硬件感知 NAS 假设固定的硬件设计并仅探索神经架构搜索空间不同，我们的框架同时探索架构搜索空间和硬件设计空间，以确定最佳的神经架构和硬件对，从而最大限度地提高测试精度和硬件效率。这种做法极大地打开了设计自由度，并推动了硬件效率和测试精度之间的帕累托边界，以实现更好的设计权衡。该框架迭代地执行两级（快速和慢速）探索。无需长时间的训练，快速探索可以有效地微调超参数并在硬件规格方面修剪劣质架构，从而显着加速 NAS 进程。然后，缓慢探索在验证集上训练候选者，并使用强化学习更新控制器，以最大限度地提高预期精度和硬件效率。在 ImageNet 上的实验表明，与最先进的网络相比，我们的协同探索 NAS 可以以相同的精度找到神经架构和相关的硬件设计，吞吐量提高 35.24%，能源效率提高 54.05%，搜索时间缩短 136 倍硬件感知 NAS。缓慢探索在验证集上训练候选者，并使用强化学习更新控制器，以最大限度地提高预期精度和硬件效率。在 ImageNet 上的实验表明，与最先进的网络相比，我们的协同探索 NAS 可以以相同的精度找到神经架构和相关的硬件设计，吞吐量提高 35.24%，能源效率提高 54.05%，搜索时间缩短 136 倍硬件感知 NAS。缓慢探索在验证集上训练候选者，并使用强化学习更新控制器，以最大限度地提高预期精度和硬件效率。在 ImageNet 上的实验表明，与最先进的网络相比，我们的协同探索 NAS 可以以相同的精度找到神经架构和相关的硬件设计，吞吐量提高 35.24%，能源效率提高 54.05%，搜索时间缩短 136 倍硬件感知 NAS。