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ISSCC 2024Session 20 · MACHINE LEARNING ACCELERATORSAI / ML

A 28nm Physics Computing Unit Supporting Emerging Physics-Informed Neural Network and Finite Element Method for Real-Time Scientific Computing on Edge Devices

📄 原文摘要

The demand for real-time computing on edge devices from emerging applications, e.g. AI, has exploded in recent years. Lately, physics-based scientific computing has also drawn significant interests driven by the growth of real-time applications, e.g., VR, IoT, robotics, etc. Fig. 20.4.1 shows examples of real-time physics-based computation including structural deformation in photorealistic VR/MR, robot dynamic control, temperature monitoring in additive manufacturing, and real-time leak-gas tracking. Unfortunately, hardware support for numerical scientific computing on edge devices is relatively poor, hindering the use of high-accuracy, high-resolution physics-based computing in real time. Figure 20.4.1 shows an example of beam deformation analysis in VR/MR falling short of a real-time latency target using classic solvers due to the large number of iterations for convergence. Recently, ASIC solvers have been designed to

👥 作者与机构

Yuhao Ju, Ganqi Xu, Jie Gu

Northwestern University, Evanston, IL

分类:AI / ML · 年份:ISSCC 2024