HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention by automating the design of DNNs deployed in more resource-constrained daily life devices. Despite its promising performance, developing optimal HW-NAS solutions can be prohibitively challenging as it requires cross-disciplinary knowledge in the algorithm, micro-architecture, and device-specific compilation. First, to determine the hardware-cost to be incorporated into the NAS process, existing works mostly adopt either pre-collected hardware-cost look-up tables or device-specific hardware-cost models. Both of them limit the development of HW-NAS innovations and impose a barrier-to-entry to non-hardware experts. Second, similar to generic NAS, it can be notoriously difficult to benchmark HW-NAS algorithms due to their significant required computational resources and the differences in adopted search spaces, hyperparameters, and hardware devices. To this end, we develop HW-NAS-Bench, the first public dataset for HW-NAS research which aims to democratize HW-NAS research to non-hardware experts and make HW-NAS research more reproducible and accessible. To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance of all the networks in the search spaces of both NAS-Bench-201 and FBNet, on six hardware devices that fall into three categories (i.e., commercial edge devices, FPGA, and ASIC). Furthermore, we provide a comprehensive analysis of the collected measurements in HW-NAS-Bench to provide insights for HW-NAS research. Finally, we demonstrate exemplary user cases to (1) show that HW-NAS-Bench allows non-hardware experts to perform HW-NAS by simply querying it and (2) verify that dedicated device-specific HW-NAS can indeed lead to optimal accuracy-cost trade-offs. The codes and all collected data are available at https://github.com/RICE-EIC/HW-NAS-Bench.
翻译:通过将部署在资源更加紧缺的日常生活装置中的DNN的设计自动化,最近人们非常关注这些设计。尽管其表现令人乐观,但开发最佳的HW-NAS解决方案可能具有令人望而却步的挑战性,因为它要求在算法、微建筑和具体装置汇编方面拥有跨学科的知识。首先,为了确定应纳入NAS进程的硬件成本,现有工作大多采用预先收集的硬件成本查看表或设备专用的硬件成本模型。 目前的工作大多采用预先收集的硬件成本查看表(1)或HW-NAS高级分类的硬件成本模型。两者都限制了HW-NAS创新的开发,并给非硬件专家设置了全面的进入障碍。 其次,与通用的NAS相似,由于需要大量计算资源,以及被采纳的搜索空间、超光谱仪和硬件装置的差异,我们开发的HW-NAS-系统所有可存取的计算机交易案例,在HW-NAS-NAS高级交易中首次公开数据数据集,旨在将HNAS-NAS的研究民主化,我们测量的硬件设计专家最终展示了HS-S-RO-S的硬件设计专家。