利用单芯片非线性声波器件进行频域并行计算

Jun Ji, Zichen Xi, Bernadeta R. Srijanto, Ivan I. Kravchenko, Ming Jin, Wenjie Xiong, Linbo Shao
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引用次数: 0

摘要

乘法累加(MAC)是信号处理、数值模拟和机器学习中的一项重要计算操作。这项研究利用千兆赫兹(GHz)频率的声波非线性,提出了可升级、可编程的频域并行计算。通过对频域数据进行编码,单个非线性声波设备可同时执行十亿次算术运算。在铌酸锂 (LN) 上占地面积为 0.03mm$^2$ 的单个器件可实现每秒 0.0144 太浮点运算 (TFLOPS),计算面积密度为 0.48 TFLOPS/mm$^2$,内核能效为 0.14 TFLOPS/瓦。作为应用,我们演示了两个 16×16 矩阵的乘法运算和 128×128 像素照片的卷积成像处理。我们的技术可以在近传感器信号处理和边缘计算领域找到多种应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frequency-domain Parallel Computing Using Single On-Chip Nonlinear Acoustic-wave Device
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 mm$^2$ on lithium niobate (LN) achieves 0.0144 tera floating-point operations per second (TFLOPS), leading to a computing area density of 0.48 TFLOPS/mm$^2$ 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.
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