Low cost and power CNN/deep learning solution for automated driving

Mihir Mody, Kumar Desappan, P. Swami, Manu Mathew, S. Nagori
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引用次数: 3

Abstract

Automated driving functions, like highway driving and parking assist, are increasingly getting deployed in high-end cars with the ultimate goal of realizing self-driving car using Deep learning techniques like convolution neural network (CNN). For mass-market deployment, the embedded solution is required to address the right cost and performance envelope along with security and safety. In the case of automated driving, one of the key functionality is “finding drivable free space”, which is addressed using deep learning techniques like CNN. These CNN networks pose huge computing requirements in terms of hundreds of GOPS/TOPS (Giga or Tera operations per second), which seems beyond the capability of today's embedded SoC. This paper covers various techniques consisting of fixed-point conversion, sparse multiplication, fusing of layers and network pruning, for tailoring on the embedded solution. These techniques are implemented on the device by means of optimized Deep learning library for inference. The paper concludes by demonstrating the results of a CNN network running in real time on TI's TDA2X embedded platform producing a high-quality drivable space output for automated driving.
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用于自动驾驶的低成本和功耗CNN/深度学习解决方案
高速公路驾驶、停车辅助等自动驾驶功能越来越多地部署在高端汽车上,最终目标是利用卷积神经网络(CNN)等深度学习技术实现自动驾驶汽车。对于大众市场部署,嵌入式解决方案需要解决适当的成本和性能以及安全性问题。就自动驾驶而言,其中一个关键功能是“寻找可驾驶的自由空间”,这是通过CNN等深度学习技术来解决的。这些CNN网络提出了数以百计的GOPS/TOPS(每秒千兆或兆级操作)的巨大计算需求,这似乎超出了当今嵌入式SoC的能力。本文介绍了各种技术,包括不动点转换、稀疏乘法、层融合和网络修剪,以定制嵌入式解决方案。这些技术通过优化的深度学习推理库在设备上实现。论文最后展示了CNN网络在TI的TDA2X嵌入式平台上实时运行的结果,为自动驾驶产生了高质量的可驾驶空间输出。
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