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arXiv Machine Learning By Zhihui Gao, Shi-Yuan Ma, Yiran Chen, Dirk Englund, Tingjun Chen

Radio-Frequency Convolutional Neural Networks

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The paper introduces Radio‑Frequency Convolutional Neural Networks (RF‑CNNs), which repurpose the frequency mixer in wireless radios to perform convolutional neural network inference directly on edge devices. By mapping multi‑channel convolutions onto frequency tones, the passive mixer can execute the entire operation in a single pass, enabling deep CNNs with up to 26.4 million parameters and nine layers to run on smartphones, wearables, and drones. Experimental results show near full‑precision performance while reducing energy consumption to 0.72 fJ per multiply‑accumulate—two orders of magnitude lower than adding a digital processor. "whyItMatters":"The approach leverages existing radio hardware to deliver efficient, state‑of‑the‑art AI inference on billions of devices without increasing size, weight, power, or cost."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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