集成电路与嵌入式系统2026，Vol.26Issue(3)：72-80,9.DOI:10.20193/j.ices2097-4191.2025.0108
基于异构协同计算的智能垃圾分类系统设计
Design of an intelligent waste sorting system based on heterogeneous collaborative computing
王智鹏 1李文斌 1李国勇2
作者信息
- 1. 中国科学院 成都计算机应用研究所,成都 610213||中国科学院大学,北京 100049
- 2. 中国科学院 成都计算机应用研究所,成都 610213
- 折叠
摘要
Abstract
The global issue of"garbage encircling cities"is intensifying,making intelligent waste sorting a research hotspot for tackling this challenge.However,embedded platforms commonly face the trade-off dilemma of"limited computing power-high real-time require-ments-optimal recognition accuracy".The traditional approaches struggle to meet practical demands:cloud-based architectures suffer from high latency due to data transmission,pure embedded architectures lack sufficient computing power,and cloud-edge collaborative architectures still exhibit interaction delays.This paper proposes a heterogeneous collaborative computing architecture based on FPGA-STM32.The FPGA handles image preprocessing and parallel convolution computations,while the STM32 manages fully connected layer operations and classification decisions.Concurrently,a lightweight convolutional neural network is optimized through pruning into a"single convolution layer+three fully connected layers"structure,incorporating INT16 quantization and clipping mechanisms to bal-ance accuracy and hardware adaptability.The experiments demonstrate that the system achieves an 83.33%accuracy rate in identifying ten categories of household waste.Compared to the MATLAB platform,it accelerates inference by 15.675 times with a processing la-tency of only 40.004 ms.The low FPGA core resource utilization enables efficient deployment in embedded waste sorting scenarios such as communities and households.关键词
异构协同计算/轻量化 CNN/FPGA-STM32架构/神经网络部署/智能垃圾分类系统/推理加速Key words
heterogeneous collaborative computing/lightweight CNN/FPGA-STM32 architecture/neural network deployment/intel-ligent waste sorting system/inference acceleration分类
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王智鹏,李文斌,李国勇..基于异构协同计算的智能垃圾分类系统设计[J].集成电路与嵌入式系统,2026,26(3):72-80,9.
