Integrated photonic-electronic platform for real-time analog data processing in LiDARs

Abstract

Light, detection and ranging (LiDAR), has been emerging as a powerful tool for applications with accurate and reliable perception requirements, e.g., autonomous driving which needs a combination of long-range and high spatial resolution together with a real-time performance. Processing the raw LiDAR data, which is a large-dimensional unstructured 3D point cloud, is computationally costly due to the nature of the algorithms used for processing the point clouds. In particular, the neural networks employed for LiDAR data processing comprise several layers, for each of which multiplications of matrices with large sizes need to be performed. In this case, graphics processing units (GPUs) cannot be used as real-time standalone devices for hardware acceleration because they have high execution time due to their dependency on a central processing unit (CPU) for data offloading and scheduling the execution of the algorithms used to process point clouds. To address the aforementioned challenges, we propose an efficient co-design of an analog neural network (ANN) and a hybrid CMOS-Photonics platform for LiDAR systems. The proposed architecture exploits the high bandwidth and low latency of optical computation to significantly improve the computational efficiency. In particular, in our proposed architecture, a CMOS control chip integrated with a photonic broadcast-and-weight architecture is interfaced with LiDAR to perform real-time data processing and high-dimensional matrix multiplications. Moreover, by processing the raw LiDAR data in the analog domain, the proposed hybrid electro-optic computing platform minimizes the number of data converters in LiDAR systems.