ArticleScience advances2025
Real-time signal processing enabled by fused networks on a memristor-based system on a chip.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Analog neural network-based wireless receiver.Science advances · 2026Article
- Multi-Modal Low-Power Adaptive Braille Recognition System Based on HfAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Reconfigurable ferroelectric transistor array for embodied neuromorphic vision with hardware-native sensing, computing, and activation.Science advances · 2026Article
- Multisensory Neuromorphic Devices: From Physics to Integration.Nano-micro letters · 2026Review
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The von Neumann bottleneck has led to a substantial rise in energy consumption of computing hardware and memory systems, particularly for data-intensive tasks like signal processing. Memristor-based in-memory computing offers an efficient alternative by performing computations within analog memory. Here, we demonstrate real-time signal processing using a fused network that combines the real-time discrete Fourier transform (DFT) and convolutional neural network (CNN) on a memristor-based analog system on a chip (SoC). A 128-by-128 memristor crossbar array performs the DFT on audio signals with a peak signal-to-noise ratio of 33.49 dB, while the following CNN classifies the resulting spectrograms with 94.72% accuracy on the AudioMNIST dataset. In addition, convolution-based edge detection is applied to real-time video frames. The SoC offers substantial energy efficiency improvement over traditional digital systems in signal processing tasks. This work highlights the potential of memristor-based SoCs for efficient real-time signal processing.
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Registered trials
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