PolySA

• DOI: 10.1145/3240765.3240838
• Publication Date: 2018-11-06T13:36:57Z
• Authors: Jason Cong; Jie Wang
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Automatic systolic array generation has long been an interesting topic due to the need to reduce the lengthy development cycles of manual designs. Existing automatic systolic array generation approach builds dependency graphs from algorithms, and iteratively maps computation nodes in the graph into processing elements (PEs) with time stamps that specify the sequences of nodes that operate within the PE. There are a number of previous works that implemented the idea and generated designs for ASICs. However, all of these works relied on human intervention and usually generated inferior designs compared to manual designs. In this work, we present our ongoing compilation framework named PolySA which leverages the power of the polyhedral model to achieve the end-to-end compilation for systolic array architecture on FPGAs. PolySA is the first fully automated compilation framework for generating high-performance systolic array architectures on the FPGA leveraging recent advances in high-level synthesis. We demonstrate PolySA on two key applications---matrix multiplication and convolutional neural network. PolySA is able to generate optimal designs within one hour with performance comparable to state-of-the-art manual designs.