ITU Journal: ICT Discoveries

The ITU Journal: ICT Discoveries publishes original research on ICT technical developments and their policy and regulatory, economic, social and legal dimensions. It builds bridges between disciplines, connects theory with application, and stimulates international dialogue. This interdisciplinary approach reflects ITU’s comprehensive field of interest and explores the convergence of ICT with other disciplines. It also features review articles, best practice implementation tutorials and case studies. The ITU Journal welcomes submissions at any time, on any topic within its scope.


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Reconfigurable processor for deep learning in autonomous vehicles

The rapid growth of civilian vehicles has stimulated the development of advanced driver assistance systems (ADASs) to be equipped in-car. Real-time autonomous vision (RTAV) is an essential part of the overall system, and the emergence of deep learning methods has greatly improved the system quality, which also requires the processor to offer a computing speed of tera operations per second (TOPS) and a power consumption of no more than 30 W with programmability. This article gives an overview of the trends of RTAV algorithms and different hardware solutions, and proposes a development route for the reconfigurable RTAV accelerator. We propose our field programmable gate array (FPGA) based system Aristotle, together with an all-stack software-hardware co design workflow including compression, compilation, and customized hardware architecture. Evaluation shows that our FPGA system can realize real-time processing on modern RTAV algorithms with a higher efficiency than peer CPU and GPU platforms. Our outlook based on the ASIC-based system design and the ongoing implementation of next generation memory would target a 100 TOPS performance with around 20 W power.


Keywords: deep learning, advanced driver assistance system (ADAS), computer vision, reconfigurable processor, autonomous vehicles
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