论文标题
使用不完美的光学硬件加速器完美执行机器学习任务
Perfectly Perform Machine Learning Task with Imperfect Optical Hardware Accelerator
论文作者
论文摘要
光学体系结构已成为一种节能和高通量硬件平台，以加速现代机器学习（ML）算法中计算密集的一般矩阵矩阵乘法（GEMM）。但是，大规模光电设备中不可避免的不完美和不均匀性阻止了光学体系结构的可扩展部署，尤其是那些具有创新性纳米设备的光学体系结构。在这里，我们报告了一个光学ML硬件，以基于级联的空间调制器加速GEMM操作，并提出一个校准程序，尽管设备和系统中的不均匀性和不完美，但仍可以准确计算。我们进一步表征了电气接口不同配置下的硬件计算精度。最后，我们部署了开发的光学硬件和校准过程，以执行ML任务，以预测单壁碳纳米管中的子带等离子频率。从光学硬件中获得的预测准确性与使用通用电子图形过程单元获得的预测准确性非常吻合。
Optical architectures have been emerging as an energy-efficient and high-throughput hardware platform to accelerate computationally intensive general matrix-matrix multiplications (GEMMs) in modern machine learning (ML) algorithms. However, the inevitable imperfection and non-uniformity in large-scale optoelectronic devices prevent the scalable deployment of optical architectures, particularly those with innovative nano-devices. Here, we report an optical ML hardware to accelerate GEMM operations based on cascaded spatial light modulators and present a calibration procedure that enables accurate calculations despite the non-uniformity and imperfection in devices and system. We further characterize the hardware calculation accuracy under different configurations of electrical-optical interfaces. Finally, we deploy the developed optical hardware and calibration procedure to perform a ML task of predicting the intersubband plasmon frequency in single-wall carbon nanotubes. The obtained prediction accuracy from the optical hardware agrees well with that obtained using a general purpose electronic graphic process unit.
