The benefits of using variable-length pipelined operations in high-level synthesis

Author:

Ben-Asher Yosi1,Rotem Nadav1

Affiliation:

1. University of Haifa

Abstract

Current high-level synthesis systems synthesize arithmetic units of a fixed known number of stages, and the scheduler mainly determines when units are activated. We focus on scheduling techniques for the high-level synthesis of pipelined arithmetic units where the number of stages of these operations is a free parameter of the synthesis. This problem is motivated by the ability to automatically create pipelined functional units, such as multipliers, with different pipe lengths. These units have different characteristics in terms of parallelism level, clock latency, frequency, etc. This article presents the Variable-length Pipeline Scheduler (VPS). The ability to synthesize variable-length pipelined units expands the known scheduling problem of high-level synthesis to include a search for a minimal number of hardware units (operations) and their desired number of stages. The proposed search procedure is based on algorithms that find a local minima in a d -dimensional grid, thus avoiding the need to evaluate all possible points in the space. We have implemented a C language compiler for VPS targeting FPGAs. Our results demonstrate that using variable-length pipeline units can reduce the overall resource usage and improve the execution time when synthesized onto an FPGA. The proposed search is sufficiently fast, taking only a few seconds, allowing an interactive mode of work. A comparison with xPilot shows a significant saving of hardware resources while maintaining comparable execution times of the resulting circuits. This work is an extension of a previous paper [Ben-Asher and Rotem 2008]

Funder

Israel Ministry of Science-Technology

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture,Software

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Advanced Implementation of DNN Translator using ResNet9 for Edge Devices;International Journal of Networking and Computing;2024

2. A Deep Neural Network Translator for Edge Site Implementation;2023 Eleventh International Symposium on Computing and Networking Workshops (CANDARW);2023-11-27

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