Frequency-domain Parallel Computing Using Single On-Chip Nonlinear Acoustic-wave Device

TL;DR

This paper introduces a GHz-frequency nonlinear acoustic-wave device capable of performing massive parallel multiply-accumulate operations in the frequency domain, enabling high-density, energy-efficient signal processing for edge computing.

Contribution

It presents a novel frequency-domain parallel computing approach using a single nonlinear acoustic-wave device with GHz operation, achieving high throughput and efficiency.

Findings

Performs a billion operations simultaneously in the frequency domain.

Achieves 0.0144 TFLOPS with a tiny 0.03 mm² device footprint.

Demonstrates matrix multiplication and image processing applications.

Abstract

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.