Embedded Deep Learning Prototyping Approach for Cyber-Physical Systems: Smart LIDAR Case Study

Quentin Cabanes,Amar Ramdane-Cherif,Benaoumeur Senouci

doi:10.3390/jsan10010018

Quentin Cabanes, Amar Ramdane-Cherif + Show 1 more

Open Access

https://doi.org/10.3390/jsan10010018

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Abstract

Cyber-Physical Systems (CPSs) are a mature research technology topic that deals with Artificial Intelligence (AI) and Embedded Systems (ES). They interact with the physical world via sensors/actuators to solve problems in several applications (robotics, transportation, health, etc.). These CPSs deal with data analysis, which need powerful algorithms combined with robust hardware architectures. On one hand, Deep Learning (DL) is proposed as the main solution algorithm. On the other hand, the standard design and prototyping methodologies for ES are not adapted to modern DL-based CPS. In this paper, we investigate AI design for CPS around embedded DL. The main contribution of this work is threefold: (1) We define an embedded DL methodology based on a Multi-CPU/FPGA platform. (2) We propose a new hardware design architecture of a Neural Network Processor (NNP) for DL algorithms. The computation time of a feed forward sequence is estimated to 23 ns for each parameter. (3) We validate the proposed methodology and the DL-based NNP using a smart LIDAR application use-case. The input of our NNP is a voxel grid hardware computed from 3D point cloud. Finally, the results show that our NNP is able to process Dense Neural Network (DNN) architecture without bias.

Highlights

We propose to share our experiments on a smart LIDAR for object classification application and the results of this experiment
We share our experience of the design/implementation and the porting of the Deep Learning (DL)-based Neural Network Processor (NNP) on a real hardware Multi-CPU/FPGA platform (Zynq)
Results related to the NNP performances are presented, and the whole methodology is validated with a smart LIDAR for pedestrian detection case study

Summary

Introduction

Cyber-Physical Systems (CPS) interact with the physical world by analyzing their environment using a variety of sensors. For this purpose, a powerful analysis tool is needed, such as Artificial Intelligence (AI), or more precisely Deep Learning (DL). If we want to build an application using specialized hardware processing for NN (e.g., FPGA [Field-Programmable Gate Array], ASIC [Application-Specific Integrated Circuit]), we need a complete design methodology for embedded DL in order to speed up the prototyping. We introduce a new methodology for smart applications in CPS around DL technologies. We validate the methodology with a smart LIDAR (LIght Detection And Ranging) application case study. We share our experiences and the difficulties

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