Hardware and neural architecture co-search that automatically generates
Artificial Intelligence (AI) solutions from a given dataset is promising to
promote AI democratization; however, the amount of time that is required by
current co-search frameworks is in the order of hundreds of GPU hours for one
target hardware. This inhibits the use of such frameworks on commodity
hardware. The root cause of the low efficiency in existing co-search frameworks
is the fact that they start from a "cold" state (i.e., search from scratch). In
this paper, we propose a novel framework, namely HotNAS, that starts from a
"hot" state based on a set of existing pre-trained models (a.k.a. model zoo) to
avoid lengthy training time. As such, the search time can be reduced from 200
GPU hours to less than 3 GPU hours. In HotNAS, in addition to hardware design
space and neural architecture search space, we further integrate a compression
space to conduct model compressing during the co-search, which creates new
opportunities to reduce latency but also brings challenges. One of the key
challenges is that all of the above search spaces are coupled with each other,
e.g., compression may not work without hardware design support. To tackle this
issue, HotNAS builds a chain of tools to design hardware to support
compression, based on which a global optimizer is developed to automatically
co-search all the involved search spaces. Experiments on ImageNet dataset and
Xilinx FPGA show that, within the timing constraint of 5ms, neural
architectures generated by HotNAS can achieve up to 5.79% Top-1 and 3.97% Top-5
accuracy gain, compared with the existing ones.

该论文提出了一种名为 HotNAS 的新颖框架，通过从现有的预训练模型（即模型库）开始，以 “热” 状态启动搜索过程，从而避免了漫长的训练时间，从而将搜索时间从 200 GPU 小时缩短为不到 3 GPU 小时。在 HotNAS 中，我们将硬件设计空间、神经体系结构搜索空间和压缩空间相互集成，以通过模型压缩来创建新的减少延迟的机会，而同时也带来了挑战。论文通过 ImageNet 数据集和 Xilinx FPGA 的实验表明，在 5ms 的时间约束下，HotNAS 生成的神经体系结构的 Top-1 和 Top-5 精度与现有的神经体系结构相比，可以实现高达 5.79% 和 3.97% 的精度提高。