由信息与通信工程学院主办的“信通论坛”本次邀请到南方科技大学安丰伟副教授到校交流，具体安排如下，欢迎感兴趣的师生参加。
一、主 题：Energy-efficient VLSI design of image recognition and machine learning algorithms for automotive applications
二、主讲人：安丰伟（副教授，南方科技大学）
三、时 间：2019年11月12日（周二）下午15:00
四、地 点：清水河校区科研楼B区302
五、内容简介：
Nowadays, many computer-vision and machine-learning applications in portable devices such as robotics, smartphones, and autonomous vehicles are constrained by their real-time performance requirements. Meanwhile, the power consumption is also a critical factor for battery-powered systems. The VLSI design for image recognition and machine learning must take account of the processing speed and energy-efficiency simultaneously. In particular, embedded SRAM (static random-access memory) macros are important power-dissipation sources in image processing algorithms due to the high-capacitance buses and frequent accesses. To avoid the inductive bounce noise on the power supply and ground caused by large in rush/discharge currents in mode transitions of power-gating approach, in this research, a hardware-friendly algorithm with optimized SRAM utilization is developed to image recognition and machine learning algorithms. The prototype chips with energy-efficient VLSI architecture have been silicon-proved in 28/65/180nm CMOS technology with high integration density and memory-utilization efficiency for demonstrating the applicability in portable devices.
六、主讲人简介：
FENGWEI AN received the Ph.D. from Hiroshima University, Japan in 2013. He worked with the Graduate School of Engineering, Hiroshima University as an Assistant Professor from 2013 and as an Associate Professor from 2017. Then, he worked with Panasonic Semiconductor Solutions Co., Ltd for DSP design of CIS and Time-of-Flight Cameras. From March 2019, he is an Associate Professor with School of Microelectronics, Southern University of Science and Technology. His research interests include reconfigurable computing, ultra-low power digital circuits, and systems, and embedded system architecture for image recognition and machine learning algorithms.
七、主办单位：信息与通信工程学院
