FPGA Implementation of a Convolutional Recurrent Neural Network for Real-Time Sensor Data Processing

被引：0

作者：

Testa, Riccardo ^{[1
]}

Yaacoub, Mohamad ^{[1
]}

Gianoglio, Christian ^{[1
]}

Valle, Maurizio ^{[1
]}

机构：

[1] Univ Genoa, Dept Naval Elect Elect & Telecommun Engn, Genoa, Italy

来源：

PROCEEDINGS OF SIE 2024 | 2025年 / 1263卷

关键词：

Tiny Machine Learning; Field Programmable Gate Array (FPGA); Artificial Skin;

D O I：

10.1007/978-3-031-71518-1_30

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a shallow Convolutional Recurrent Neural Network (C-RNN) that directly employs raw signals for classification, implemented on a Field Programmable Gate Array (FPGA) for artificial texture classification. Data was collected using a tactile sensing system based on piezoelectric polymers for eight artificial textures. Preliminary experimental results show that the neural network achieves an accuracy of 96.54%, and the implementation utilizes a total of 208,441 LUTs, 1,247 DSPs, 74,679 flip-flops, and 306 BRAM. The estimated latency is 1,242 clock cycles, corresponding to 6.21 mu s with a 200 MHz clock used in the synthesis.

引用

页码：258 / 265

页数：8

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