Streaming Convolutional Neural Network FPGA architecture for RFSoC data converters

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MacLellan, Andrew and Crockett, Louise H. and Stewart, Robert W. (2023) Streaming Convolutional Neural Network FPGA architecture for RFSoC data converters. In: 21st IEEE Interregional New Circuit and Systems (NEWCAS) Conference, 2023-06-26 - 2023-06-28, John McIntyre Conference Centre.

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Abstract

This paper presents a novel Convolutional Neural Network (CNN) FPGA architecture designed to perform processing of radio data in a streaming manner without interruption. The proposed architecture is evaluated for radio modulation classification tasks implemented on an AMD RFSoC 2x2 development board and operating in real-time. The proposed architecture leverages optimisation such as the General Matrix-to-Matrix (GEMM) transform, on-chip weights, fixed-point arithmetic, and efficient utilisation of FPGA resources to achieve constant processing of a stream of samples. The performance of the proposed architecture is demonstrated through accuracy results obtained during live modulation classification, while operating at a sampling frequency of 128 MHz before decimation. The proposed architecture demonstrates promising results for real-time, time-critical CNN applications.

ORCID iDs

MacLellan, Andrew ORCID logoORCID: https://orcid.org/0000-0001-9624-2212, Crockett, Louise H. ORCID logoORCID: https://orcid.org/0000-0003-4436-0254 and Stewart, Robert W. ORCID logoORCID: https://orcid.org/0000-0002-7779-8597;
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