DIAN: differentiable accelerator-network co-search towards maximal DNN efficiency

We present DIAN, a Differentiable Accelerator-Network Co-Search framework for automatically searching for matched networks and accelerators to maximize both the accuracy and efficiency. Specifically, DIAN integrates two enablers: (1) a generic design space for DNN accelerators that is applicable to both FPGA- and ASIC-based DNN accelerators; and (2) a joint DNN network and accelerator co-search algorithm that enables the simultaneous search for optimal DNN structures and their accelerators. Experiments and ablation studies based on FPGA measurements and ASIC synthesis show that the matched networks and accelerators generated by DIAN consistently outperform state-of-the-art (SOTA) DNNs and DNN accelerators (e.g., 3.04× better FPS with a 5.46% higher accuracy on ImageNet), while requiring notably reduced search time (up to $1234.3\times)$ over SOTA co-exploration methods, when evaluated over ten SOTA baselines on three datasets.