SATAY: a streaming architecture toolflow for accelerating YOLO models on FPGA devices
File(s)
Accepted version
Author(s)
Montgomerie-Corcoran, Alexander
Toupas, Petros
Yu, Zhewen
Bouganis, Christos-Savvas
Type
Conference Paper
Abstract
AI has led to significant advancements in computer vision and image processing tasks, enabling a wide range of applications in real-life scenarios, from autonomous vehicles to medical imaging. Many of those applications require efficient object detection algorithms and complementary real-time, low latency hardware to perform inference of these algorithms. The YOLO family of models is considered the most efficient for object detection, having only a single model pass. Despite this, the complexity and size of YOLO models can be too computationally demanding for current edge-based platforms. To address this, we present SATAY: a Streaming Architecture Toolflow for Accelerating YOLO. This work tackles the challenges of deploying state-of-the-art object detection models onto FPGA devices for ultralow latency applications, enabling real-time, edge-based object detection. We employ a streaming architecture design for our YOLO accelerators, implementing the complete model on-chip in a deeply pipelined fashion. These accelerators are generated using an automated toolflow, and can target a range of suitable FPGA devices. We introduce novel hardware components to support the operations of YOLO models in a dataflow manner, and off-chip memory buffering to address the limited on-chip memory resources. Our toolflow is able to generate accelerator designs which demonstrate competitive performance and energy characteristics to GPU devices, and which outperform current state-of-the-art FPGA accelerators. The code is available at https://github.com/ICIdsl/satay
Date Issued
2024-02-01
Date Acceptance
2023-12-01
Citation
2023 International Conference on Field Programmable Technology (ICFPT), 2024, pp.179-187
ISBN
979-8-3503-5911-4
ISSN
2837-0430
Publisher
IEEE
Start Page
179
End Page
187
Journal / Book Title
2023 International Conference on Field Programmable Technology (ICFPT)
Copyright Statement
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
22nd International Conference on Field Programmable Technology (ICFPT)
Subjects
Engineering
Engineering, Electrical & Electronic
Science & Technology
Technology
Publication Status
Published
Start Date
2023-12-12
Finish Date
2023-12-14
Coverage Spatial
Yokohama, Japan