Abstract
High-level synthesis (HLS) is getting increasing attention from both academia and industry for high-quality and high-productivity designs. However, when inferring primitive-type arrays in HLS designs into on-chip memory buffers, commercial HLS tools fail to effectively organize FPGAs’ on-chip BRAM building blocks to realize high-bandwidth data communication; this often leads to suboptimal quality of results. This paper addresses this issue via automated on-chip buffer restructuring. Specifically, we present three buffer restructuring approaches and develop an analytical model for each approach to capture its impact on performance and resource consumption. With the proposed model, we formulate the process of identifying the optimal design choice into an integer non-linear programming (INLP) problem and demonstrate that it can be solved efficiently with the help of a one-time C-to-HDL(hardware description language) synthesis. The experimental results show that our automated source-to-source code transformation tool improves the performance of a broad class of HLS designs by averagely 4.8x.
Peipei Zhou
Assistant Professor of Engineering
My research interests include Customized Computer Architecture and Programming Abstraction for Health & AI Applications