用FPGA加速神经网络，实现98%识别率的葡萄实时检测
Red grape detection with accelerated artificial neural networks in the FPGA's programmable logic
- 基于FINN架构将4位量化MobileNet v1部署于FPGA逻辑单元
- 达到98%识别率，推理速度达6611帧/秒
- 适合需要低延迟的农业机器人视觉系统
机器人在移动中进行罐装检测时通常会减速，且为追踪算法速度常将摄像头设置为低帧率，导致任务执行时间增加。虽然AMD开发了Vitis-AI框架用于将检测算法部署至FPGA，但未能充分利用FPGA的可编程逻辑（PL）。本文采用FINN架构，在FPGA的PL中部署三种量化神经网络：4位量化的MobileNet v1、2位量化的CNV，以及1位量化的二值神经网络（BNN），模型均在自建公开数据集RG2C上训练。其中MobileNet v1表现最佳，成功率达98%，推理速度高达6611 FPS。结果证明，FPGA可有效加速神经网络，使其适用于注意力机制等实时应用。
原文摘要 · Abstract (English)
Robots usually slow down for canning to detect objects while moving. Additionally, the robot's camera is configured with a low framerate to track the velocity of the detection algorithms. This would be constrained while executing tasks and exploring, making robots increase the task execution time. AMD has developed the Vitis-AI framework to deploy detection algorithms into FPGAs. However, this tool does not fully use the FPGAs' PL. In this work, we use the FINN architecture to deploy three ANNs, MobileNet v1 with 4-bit quantisation, CNV with 2-bit quantisation, and CNV with 1-bit quantisation (BNN), inside an FPGA's PL. The models were trained on the RG2C dataset. This is a self-acquired dataset released in open access. MobileNet v1 performed better, reaching a success rate of 98 % and an inference speed of 6611 FPS. In this work, we proved that we can use FPGAs to speed up ANNs and make them suitable for attention mechanisms.
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