Performance Simulation of a Traffic Sign Recognition based Neural Network on Cadence’s Tensilica Vision P6 DSP using Xtensa Xplorer IDE

Abstract

Advanced Driver Assistance System (ADAS) technology is currently in an embryonic stage. Many multinational tech companies and startups are developing a truly autonomous vehicle that will guarantee the safety and security of the passengers and other vehicles, pedestrians on roads, and roadside structures such as traffic signal poles, traffic signposts, and other structures. However, these autonomous vehicles have not been implemented on a large scale for regular use on roads currently. These autonomous vehicles perform many different object detection/recognition tasks. Examples include traffic sign recognition, lane detection, pedestrian detection. Usually, the person driving the vehicle performs these detection/recognition tasks. The main goal of such autonomous systems should be to perform these tasks in real-time. Deep learning performs these object recognition tasks with very high accuracy. The neural network is implemented on the hardware device, which does all the computation work. Different vendors have many different hardware choices that suit the client's needs. Usually, these neural networks are implemented on a CPU, DSP, GPU, FPGA, and other custom-made AI-specific hardware. The underlying processor forms a vital part of an ADAS. The CNN needs to process the incoming frames from a camera for real-time object detection/recognition tasks. Real-time processing is necessary to take appropriate actions/decisions depending on the logic embedded. Hence knowing the performance of the neural network (in terms of frames processed per second) on the underlying hardware is a significant factor in deciding the various hardware options available from different vendors, which CNN model to implement, whether the CNN model is suitable to implement on the underlying hardware depending upon the system specifications and requirement. In this paper, we trained a CNN using the transfer learning approach to recognize german traffic signs using Nvidia DIGITS web-based software and analyzed the performance of this trained CNN (in terms of frames per second) by simulating the trained CNN on Cadence's Xtensa Xplorer software by selecting Cadence's Tensilica Vision P6 DSP as an underlying processor for inference.