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

Abstract

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 solve Poisson equation-related applications with a finite difference method (FDM), but have trouble handling more complex structures [1–3]. To overcome the real-time hurdle, physics-informed neural network (PINN) or physics-informed machine learning (PIML) solutions [4–7] are being developed by the scientific community, using a data-driven approach to boost the computing efficiency of physics solvers. Figure 20.4.1 shows PINN solutions can reach 1900-10000× speedup compared with classic solvers based on Nvidia Modulus with less than 1% accuracy loss [4]. However, if numerous physics equations are to be processed by a PINN, highly diversified dataflows are needed to support a variety of PINN models, making it unfriendly to an ASIC solution. In addition, a tradeoff of speed and accuracy needs to be made between a PINN and classic numerical solutions for a specific application. To overcome these challenges, this work presents a unified physics computing unit (PhyCU) architecture supporting both PINNs and classic finite element method (FEM) solution. The highlights of PhyCU are as follows: 1) This work delivers an ASIC solution supporting inference for most major PINN models with configurable dataflow; 2) The PhyCU architecture also natively supports the classic FEM through a conjugate gradient iterative method (CG) providing a high-accuracy alternative using the same hardware; 3) Sparsity and data compression techniques for both PINN and FEM computation are developed achieving orders of magnitude latency reduction compared with a classic solution on GPU and 19.5-35.9× energy savings compared with prior ASICs.