Latte: Locality Aware Transformation for High-Level Synthesis

Abstract

Modern FPGA chips feature abundant reconfigurable resources such as LUTs, FFs, BRAMs and DSPs. High-level synthesis (HLS) further advances users productivity in designing accelerators and scaling out the design quickly via fine-grain and coarse-grain pipelining and duplication to utilize on-chip resources. However, current HLS tools fail to consider data locality in the scaled-out design; this leads to a long critical path which results in a low operating frequency. In this paper we summarize the timing degradation problems to four common collective communication and computation patterns in HLS-based accelerator design: scatter, gather, broadcast and reduce. These widely used patterns scale poorly in one-to-all or all-to-one data movements between off-chip communication interface and on-chip storage, or inside the computation logic. Therefore, we propose the Latte microarchitecture featuring pipelined transfer controllers (PTC) along data paths in these patterns. Furthermore, we implement an automated framework to apply our Latte implementation in HLS with minimal user efforts. Our experiments show that Latte-optimized designs greatly improve the timing of baseline HLS designs by 1.50x with only 3.2% LUT overhead on average, and 2.66x with 2.7% overhead at maximum.

Peipei Zhou

Assistant Professor of ECE Department

My research interests include Customized Computer Architecture and Programming Abstraction for Health & AI Applications