Kaisheng Ma The Pennsylvania State University
时间： 2018-04-10 14:00-2018-04-10 15:00
Energy harvesting has been widely investigated as a promising method of providing power for ultra-low-power applications. Such energy sources include solar energy, radio-frequency (RF) radiation, piezoelectric effect, thermal gradients, etc. However, the power supplied by these sources is highly unreliable and dependent upon ambient environment factors. Hence, it is necessary to develop specialized systems that are tolerant to this power variation, and also capable of making forward progress on the computation tasks.
In this talk, I will first do architectural explore of the design space for a nonvolatile processor with different architectures, different input power sources, and policies for maximizing forward progress. Noticing that such IoT nodes usually perform similar operations across each new input record, which provides opportunities for mining the potential information in buffered historical data, at potentially lower effort, while processing new data rather than abandoning old inputs due to limited computational energy. This approach is proposed as incidental computing, and synergies between this approach and approximation techniques is explored. Last but not least, I take fog computing in Wireless Sensor Networks (WSN) as one of the system level examples to perform optimization from programing, intra-chain and inter-chain level, and show how nonvolatility features including nonvolatile processors and nonvolatile RF can benefit the system, and how other optimizations like load balance under unstable power, as well as increasing nodes density for quality of service can be applied into the fog computing system.
Kaisheng Ma is now a Ph.D. in Department of Computer Science and Engineering, The Pennsylvania State University. His research focuses on computer architecture, especially on IoT Fog Computing architecture exploration and optimization. In the past 5 years, He has published 36 papers (18 first author), and 385 google citations (March 2018). As first author, Dr. Ma has won many awards, including: 2015 HPCA Best Paper Award, 2016 IEEE MICRO Top Picks, 2017 ASP-DAC Best Paper Award. He has many honors, including 2016 Penn State CSE Department Best Graduate Research Award (Among ~170 Ph.D. students), 2016 Cover Feature of NSF ASSIST Engineering Research Center Newsletter (Among 40 graduate students across four participating universities.), 2011 Yang Fuqing & Wang Yangyuan Academician Scholarship (1/126, Peking University.).