Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
| Autor: | Nicolò Ghielmetti, Vladimir Loncar, Maurizio Pierini, Marcel Roed, Sioni Summers, Thea Aarrestad, Christoffer Petersson, Hampus Linander, Jennifer Ngadiuba, Kelvin Lin, Philip Harris |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Physics - Instrumentation and Detectors Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition FOS: Physical sciences deep learning Machine Learning (stat.ML) Instrumentation and Detectors (physics.ins-det) computer vision semantic segmentation Machine Learning (cs.LG) Computing and Computers Human-Computer Interaction machine learning Statistics - Machine Learning Artificial Intelligence Hardware Architecture (cs.AR) FPGA hls4ml autonomous vehicles Detectors and Experimental Techniques Computer Science - Hardware Architecture Software |
| Zdroj: | Machine Learning: Science and Technology, 3 (4) |
| ISSN: | 2632-2153 |
| Popis: | In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network architecture, we demonstrate a fully-on-chip deployment with a latency of 4.9 ms per image, using less than 30% of the available resources on a Xilinx ZCU102 evaluation board. The latency is reduced to 3 ms per image when increasing the batch size to ten, corresponding to the use case where the autonomous vehicle receives inputs from multiple cameras simultaneously. We show, through aggressive filter reduction and heterogeneous quantization-aware training, and an optimized implementation of convolutional layers, that the power consumption and resource utilization can be significantly reduced while maintaining accuracy on the Cityscapes dataset. Machine Learning: Science and Technology, 3 (4) ISSN:2632-2153 |
| Databáze: | OpenAIRE |
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