{'fraction_non_alphanumeric': 0.01910828025477707, 'fraction_numerical': 0.0021231422505307855, 'mean_word_length': 4.579881656804734, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Low Cost and Portable Mini Motor Car System with a BNN Accelerator on FPGA

To realize autonomous driving, a deep neural network (DNN) is one of the key technologies. However, since DNN needs a lot of computation, it is challenging for an edge device to support DNN with limited computation resources. A binarized neural network (BNN) has been proposed to reduce latency and parameter size and is suited for hardware implementation. Since current DNN technology is a growing and better algorithm change with time, implementing DNN on an FPGA is preferable to an ASIC. In this paper, we propose a low cost and portable mini motor car system with a BNN accelerator on an FPGA. We compare the road tracking demonstration with a similar motor car using Raspberry Pi and show the effectiveness of FPGA in a DNN implementation. The proposed system is implemented on Nexys A7, one of the most popular FPGA development boards using an Artix-7 FPGA.