Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor

ELECTRONICS(2024)

引用 0|浏览1
暂无评分
摘要
Image analysis-based artificial intelligence (AI) models leveraging convolutional neural networks (CNN) take a significant role in evaluating the ripeness of strawberry, contributing to the maximization of productivity. However, the convolution, which constitutes the majority of the CNN models, imposes significant computational burdens. Additionally, the dense operations in the fully connected (FC) layer necessitate a vast number of parameters and entail extensive external memory access. Therefore, reducing the computational burden of convolution operations and alleviating memory overhead is essential in embedded environment. In this paper, we propose a strawberry ripeness classification system utilizing a convolution-based feature extractor (CoFEx) for accelerating convolution operations and an edge AI processor, Intellino, for replacing FC layer operations. We accelerated feature map extraction utilizing the CoFEx constructed with systolic array (SA) and alleviated the computational burden and memory overhead associated with the FC layer operations by replacing them with the k-nearest neighbors (k-NN) algorithm. The CoFEx and the Intellino both were designed with Verilog HDL and implemented on a field-programmable gate array (FPGA). The proposed system achieved a high precision of 93.4%, recall of 93.3%, and F1 score of 0.933. Therefore, we demonstrated a feasibility of the strawberry ripeness classification system operating in an embedded environment.
更多
查看译文
关键词
system-on-chips(SoC),hardware accelerator,artificial intelligence (AI),convolutional neural network (CNN),systolic array (SA)
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要