A new technical paper titled “Machine Intelligence on Wireless Edge Networks” was published by researchers at MIT and Duke University.
Abstract
“Deep neural network (DNN) inference on power-constrained edge devices is bottlenecked by costly weight storage and data movement. We introduce MIWEN, a radio-frequency (RF) analog architecture that “disaggregates” memory by streaming weights wirelessly and performing classification in the analog front end of standard transceivers. By encoding weights and activations onto RF carriers and using native mixers as computation units, MIWEN eliminates local weight memory and the overhead of analog-to-digital and digital-to-analog conversion. We derive the effective number of bits of radio-frequency analog computation under thermal noise, quantify the energy–precision trade-off, and demonstrate digital-comparable MNIST accuracy at orders-of-magnitude lower energy, unlocking real-time inference on low-power, memory-free edge devices.”
Find the technical paper here.. June 2025.
Vadlamani, Sri Krishna, Kfir Sulimany, Zhihui Gao, Tingjun Chen, and Dirk Englund. “Machine Intelligence on Wireless Edge Networks.” arXiv preprint arXiv:2506.12210 (2025).
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