Embedded Semantic Segmentation Network Optimized for Matrix Multiplication Accelerator
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Embedded Semantic Segmentation Network Optimized for Matrix Multiplication Accelerator

Authors: Jaeyoung Lee

Abstract:

Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: Edge network, embedded network, MMA, matrix multiplication accelerator and semantic segmentation network.

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References:


[1] S. Karen and Z. Andrew, "Very Deep Convolutional Networks for Large-Scale Image Recognition," in International Conference on Learning Representations, 2015.
[2] C. Liang-Chieh et al, "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
[3] W. Yu, Z. Quan and W. Xiaofu, "ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation," 2019.
[4] K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 770-778.
[5] R. Olaf, F. Philipp and B. Thomas, "U-Net: Convolutional Networks for Biomedical Image Segmentation, " MICCAI2015 pp. 234-241.
[6] H. Zhao, J. Shi, X. Qi, X. Wang and J. Jia, "Pyramid Scene Parsing Network," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 6230-6239.
[7] L. Chen et al, "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation," ECCV 2018, pp. 801- 818.
[8] V. Badrinarayanan, A. Kendall and R. Cipolla, "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2481-2495, 1 Dec. 2017.
[9] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018, pp. 4510-4520.
[10] A. Howard et al, "Searching for mobilenetv3," arXiv:1905.02244, 2019.
[11] T. Wu, S. Tang, R. Zhang, and Y. Zhang, "Cgnet: A light-weight context guided network for semantic segmentation," arXiv:1811.08201, 2018.
[12] X. Zhu, H. Hu, S. Lin, and J. Dai, "Deformable ConvNets v2: More Deformable, Better Results," arXiv:1811.11168, 2018.
[13] A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello, "Enet: A deep neural network architecture for real-time semantic segmentation," arXiv:1606.02147, 2016.
[14] S. Mehta, M. Rastegari, L. G. Shapiro, and H. Hajishirzi, "Espnetv2: A light-weight, power efficient, and general purpose convolutional neural network," CoRR, abs/1811.11431, 2018.
[15] T. M. Arjona-Medina et al, “Speeding up semantic segmentation for autonomous driving,” NIPS Workshop, 2016
[16] M. Liu and H. Yin, "Feature Pyramid Encoding Network for Real-time Semantic Segmentation," arXiv:1909.08599, 2019
[17] R. P. Poudel, S. Liwicki and R. Cipolla, “Fast-SCNN: Fast Semantic Segmentation Network,” arXiv:1902.04502, 2019.