Efficiently processing broadband signals using convolutional neural networks (CNNs) could enhance the performance of machine learning tools for a wide range of real-time applications, including image recognition, remote sensing and environmental monitoring. However, past studies suggest that performing broadband convolutional processing computations directly in sensors is challenging, particularly when using conventional complementary metal-oxide-semiconductor (CMOS) technology, which underpins the functioning of most existing transistors.