Multiply-accumulation (MAC) is a crucial computing operation in signal processing, numerical simulations, and machine learning. This work presents a scalable, programmable, frequency-domain parallel computing leveraging gigahertz (GHz)-frequency acoustic-wave nonlinearities. By encoding data in the frequency domain, a single nonlinear acoustic-wave device can perform a billion arithmetic operations simultaneously. A single device with a footprint of 0.03 mm2 on lithium niobate (LN) achieves 0.0144 tera floating-point operations per second (TFLOPS), leading to a computing area density of 0.48 TFLOPS/mm2 and a core power efficiency of 0.14 TFLOPS/Watt. As applications, we demonstrate multiplications of two 16-by-16 matrices and convolutional imaging processing of 128-by-128-pixel photos. Our technology could find versatile applications in near-sensor signal processing and edge computing.
@article{arxiv.2409.02689,
title = {Frequency-domain Parallel Computing Using Single On-Chip Nonlinear Acoustic-wave Device},
author = {Jun Ji and Zichen Xi and Bernadeta R. Srijanto and Ivan I. Kravchenko and Ming Jin and Wenjie Xiong and Linbo Shao},
journal= {arXiv preprint arXiv:2409.02689},
year = {2024}
}