AutoCAS

Nowadays, autonomous mobility circuits and systems (AutoCAS) have been attracting much attention because of the rapid development of electronic systems-based autonomous vehicles. They are not only used in controlling the engine but also have emerged in the areas of safety, driving assistance, and communications. Autonomous vehicle needs highly reliable circuits and systems for artificial intelligence (AI), sensing, signal processing, and V2X, etc.
Additionally, future electronic systems are moving towards making vehicles that are possible to configure the behaviors by using software easily. Circuits and systems design for autonomous mobility is an area of high investment for semiconductor companies targeting high revenue and growth potential. Our CAS society needs to increase opportunities for our members to communicate/collaborate with researchers/engineers in emerging autonomous mobility CAS directions and contribute to technological innovation and excellence.

Therefore, this workshop aims at announcing the contributions in solving the CAS-based problems for the autonomous mobility components, such as memory, sensors, ECU and deep neural network (DNN), processor, etc. Through this workshop, the researchers are able to obtain the benefit from these innovative techniques. By learning about the advantages and disadvantages of state-of-the-art methods. They can further innovate and enhance their circuits and systems for highly demanding vehicles in five to ten years. The workshop will consist of one plenary track with 6 ~ 7 invited speakers from industry and academia.

AutoCAS attendees will not be charged with any extra fee if they are IEEE CAS society members.

For registration, please contact the Conference Secretariat.
(Email : aicas2022.conf@gmail.com)
 

Embedded Artificial Intelligence Technology for ADAS/ADS Applications Prof. Jiun-In Guo National Yang Ming Chiao Tung University, Taiwan Biography Abstract Biography Prof. Jiun-In Guo received the B.S. and Ph.D. degrees in Electronics Engineering from National Chiao Tung University, Hsinchu, Taiwan, in 1989 and 1993, respectively. He is currently a Distinguished Professor of the Institute of Electronics, the Associated Dean of Electrical and Computer Engineering, and the Director of Embedded Artificial Intelligence Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. Prof. Guo has been the Director of Institute of Electronics, National Chiao-Tung University during 2013-2015, and served as the Director of SoC research center of NCTU during 2016-2019. His research interests include images, multimedia, and digital signal processing, VLSI algorithm/architecture design, digital SIP design, SOC design, and intelligent vision processing applications including ADAS/Self-driving vehicles. In the past 10 years, Prof. Guo dedicated on researching of the embedded AI technology for ADAS/ADS applications, developed over 30 technologies for ADAS and conducted over 150 industrial collaboration projects. Prof. Guo received the outstanding electrical engineering professor award from the Chinese Institute of Electrical Engineering in 2010, the outstanding engineering professor award from the Chinese Institute of Engineers in 2014, the outstanding research award of Minister of Science (MOST) in 2017, as well as two outstanding technology transferring awards of MOST in both 2018 and 2020 with the topic of AI-based ADAS systems. Prof. Guo is the author of 252 technical papers on the research areas. Abstract This talk regards about how to design deep learning model for embedded AI SoC computing platform dedicated to ADAS/ADS applications, covering the data acquisition and fast annotation, light weight model design and simplification, model quantization for being realized in an AI SoC, and a hybrid fixed point deep learning accelerator (DLA). Some ADAS/ADS design examples will be used for illustration to demonstrate the effectiveness of the mentioned embedded AI technology.

CMOS Single-photon Avalanche Diodes for Vehicle LiDAR Prof. Sheng-Di Lin National Yang Ming Chiao Tung University, Taiwan Biography Abstract Biography Sheng-Di Lin received his B.S. and M. S. degrees in physics at National Taiwan University in 1992 and 1994, respectively, and his Ph.D. degree in electronics engineering at National Chiao Tung University in 2002. In 2002-2005, he worked as a post-doctoral research associate in Cavendish laboratory of Cambridge University in United Kingdom. Joining Department of Electronics Engineering at National Chiao Tung University in 2005, his current research focuses on atomic-scale metallic films grown by molecular beam epitaxy and single-photon avalanche diodes in CMOS technology. Abstract High-performance LiDAR is a key module to enable autonomous driving. In this talk, I shall address the issues hinder LiDAR from current vehicles and how CMOS SPAD would help to address these problems including high laser power, short ranging distance, production cost, system complexity, and power consumption. Starting from a brief introduction to the single-photon avalanche diodes (SPADs) in CMOS technology, I shall present out works on device, array, circuits, and LiDAR system. A few topics regarding photon-counting LiDAR, such as minimum integration time and time-bin width of the time-to-digital converter, will also be discussed. We believe that this ultimate optical sensor could make cost of LiDAR modules effective and available to each car in the near future.