From Parallelization to Customization – Challenges and Opportunities
Jason Cong · 2021
With large-scale deployment of FPGAs in both private and public clouds in the past a few years, customizable computing is transitioning from advanced research into mainstream computing. In this talk, I shall first showcase a few big data and machine learning applications that benefit significantly from customization. Next, I shall discuss the challenges of FPGA programming for the efficient accelerator designs, which presents a significant barrier to many software programmers, despite the recent advances in high-level synthesis. Then, I shall highlight our recent progress on automated compilation for customized archictectures, such as systolic arrays, stencils, and more general CPPs (composable parallel and pipelined) architectures. I shall also present our ongoing work on HeteroCL, a highly productive multi-paradigm programming framework targeting accelerator-rich heterogeneous architectures, and is being used as a focal point to integrate various optimizaiton techniques and support high-level domain-specific languages (DSL) such as Halide and Pytorch. Our goal is to “demacratize customizable computing” so that most (if not all) software programmers can design optimized accelerators on FPGAs.