  High quality AI solutions require joint optimization of AI algorithms, such
as deep neural networks (DNNs), and their hardware accelerators. To improve the
overall solution quality as well as to boost the design productivity, efficient
algorithm and accelerator co-design methodologies are indispensable. In this
paper, we first discuss the motivations and challenges for the
Algorithm/Accelerator co-design problem and then provide several effective
solutions. Especially, we highlight three leading works of effective co-design
methodologies: 1) the first simultaneous DNN/FPGA co-design method; 2) a
bi-directional lightweight DNN and accelerator co-design method; 3) a
differentiable and efficient DNN and accelerator co-search method. We
demonstrate the effectiveness of the proposed co-design approaches using
extensive experiments on both FPGAs and GPUs, with comparisons to existing
works. This paper emphasizes the importance and efficacy of
algorithm-accelerator co-design and calls for more research breakthroughs in
this interesting and demanding area.
