CSDSE: An efficient design space exploration framework for deep neural network accelerator based on cooperative search

The design and optimization of deep neural network accelerators necessitates thoughtful consideration of numerous design parameters and various resource/physical constraints that render their design spaces massive in scale and complex in distribution. When faced with these massive and complex design spaces, previous works on design space exploration confront the exploration-exploitation dilemma, struggling to concurrently ensure optimization efficiency and stability. To address the exploration-exploitation dilemma, we present a novel design space exploration method entitled CSDSE. CSDSE implements heterogeneous agents separately accountable for exploration or exploitation to cooperatively search the design space. In order to enable CSDSE to adapt design spaces with various space distributions and expanding scales, we extend CSDSE with mechanism of adaptive agent organization and multi-scale search. Furthermore, we introduce a weighted compact buffer that encourages agents to search in diverse directions and bolsters their global exploration ability. CSDSE is implemented to optimize accelerator design. Compared to former DSE methods, it achieves latency speedups of up to 15.68x and energy-delay-product reductions of up to 16.22x under different constraint scenarios.