For further information, please contact the General chair: Tohru Ishihara
RWTH Aachen University, Germany
Scalability Aspects of a Many-Core ASIP Platform targeted at Convolutional Neural Networks
Deep Neural Networks (DNN) are an essential component for autonomous driving. Scene segmentation, road sign detection are two examples of functions which are expected to use DNNs. Important requirements - besides low classification error rate - are real time, power efficiency, and flexibility. Since algorithms continuously develop, some degree of flexibility is required, which can be addressed by using an application specific processor (ASIP). To achieve high throughput many-core ASIP solutions are required. The talk discusses scalability aspects and heuristics for many-core mapping of the software. A case study on many-core platforms with different numbers of cores provides runtime/energy/off-chip-IO trade-offs.
Gerd Ascheid received the Diploma and Ph.D. (Dr.-Ing.) degrees in electrical engineering (com- munication engineering) from RWTH Aachen University, Aachen, Germany. In 1988, he started as a Co-Founder and the Managing Director of Cadis Prüftechnik GmbH (CADIS), Heddesheim, Germany, which success- fully brought the system simulation tool COSSAP to the market. In 1994, CADIS was acquired by Syn- opsys Inc., Mountain View, CA, USA, a California- based EDA market leader. From 1994 to 2003, he was a Director/Senior Director with Synopsys Inc. In 2003, he joined the Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, as a Full Professor. He is also the Founder of several successful start-up companies. His current research interests include wireless communication algorithms, application-specific integrated platforms, in partic- ular, for mobile terminals and cyberphysical devices. He has co-authored three books, published numerous papers in the domain of digital communication algorithms, and ASIC implementation.