Explainable-DSE: An Agile and Explainable Exploration of Efficient HW/SW Codesigns of Deep Learning Accelerators Using Bottleneck Analysis

Effective design space exploration (DSE) is paramount for hardware/software codesigns of deep learning accelerators that must meet strict execution constraints. For their vast search space, existing DSE techniques can require excessive trials to obtain a valid and efficient solution because they rely on black-box explorations that do not reason about design inefficiencies. In this paper, we propose Explainable-DSE - a framework for the DSE of accelerator codesigns using bottleneck analysis. By leveraging information about execution costs from bottleneck models, our DSE is able to identify bottlenecks and reason about design inefficiencies, thereby making bottleneck-mitigating acquisitions in further explorations. We describe the construction of bottleneck models for DNN accelerators. We also propose an API for expressing domain-specific bottleneck models and interfacing them with the DSE framework. Acquisitions of our DSE systematically cater to multiple bottlenecks that arise in executions of multi-functional workloads or multiple workloads with diverse execution characteristics. Evaluations for recent computer vision and language models show that Explainable-DSE mostly explores effectual candidates, achieving codesigns of 6x lower latency in 47x fewer iterations vs. non-explainable DSEs using evolutionary or ML-based optimizations. By taking minutes or tens of iterations, it enables opportunities for runtime DSEs.