Lightning Talk: The Next Wave of High-level Synthesis

Recent works established new High-Level Synthesis (HLS) solutions translating AI models described in PyTorch to customized AI accelerators automatically. By adopting PyTorch as input for AI designs (instead of traditional C/C++ for HLS), the lines of code and design simulation time can be reduced by about 10× and 100×, respectively. Such AI model-to-RTL flows pave the way for a new wave of HLS that could drive the high-productivity designs of AI circuits with high-density, high-energy efficiency, low cost, and short design cycle. And such high-level model-to-RTL flows can be expanded to other non-AI domains. Meanwhile, we are also facing existing and new challenges for such HLS solutions, such as ensuring the correctness of the high-level design, accommodating accurate low-level timing/energy information, handling the complexity of 3D circuits and/or chiplet-based design flows, and achieving all these in a highly scalable manner. In this paper, we share the state-of-the-art solutions, limitations, and new opportunities facing the emergence of a new wave of the next-generation HLS.