  In this paper, we present a novel multi-objective hardware-aware neural
architecture search (NAS) framework, namely HSCoNAS, to automate the design of
deep neural networks (DNNs) with high accuracy but low latency upon target
hardware. To accomplish this goal, we first propose an effective hardware
performance modeling method to approximate the runtime latency of DNNs on
target hardware, which will be integrated into HSCoNAS to avoid the tedious
on-device measurements. Besides, we propose two novel techniques, i.e., dynamic
channel scaling to maximize the accuracy under the specified latency and
progressive space shrinking to refine the search space towards target hardware
as well as alleviate the search overheads. These two techniques jointly work to
allow HSCoNAS to perform fine-grained and efficient explorations. Finally, an
evolutionary algorithm (EA) is incorporated to conduct the architecture search.
Extensive experiments on ImageNet are conducted upon diverse target hardware,
i.e., GPU, CPU, and edge device to demonstrate the superiority of HSCoNAS over
recent state-of-the-art approaches.
