Emerging Hardware Techniques and EDA Methodologies for Neuromorphic Computing (Dagstuhl Seminar 19152)

Krishnendu Chakrabarty, Tsung-Yi Ho, Hai Li, Ulf Schlichtmann, Michael Wagner
2019 Dagstuhl Reports  
This report documents the program and the outcomes of Dagstuhl Seminar 19152 "Emerging Hardware Techniques and EDA Methodologies for Neuromorphic Computing," which was held during April 7-10, 2019 in Schloss Dagstuhl -Leibniz Center for Informatics. Though interdisciplinary considerations of issues from computer science in the domain of machine learning and large scale computing have already successfully been covered in a series of Dagstuhl seminars, this was the first time that Neuromorphic
more » ... puting was brought out as the focus. During the seminar, many of the participants presented their current research on the traditional and emerging hardware techniques, design methodologies, electronic design automation techniques, and application of neuromorphic computing, including ongoing work and open problems. This report documents the abstracts or extended abstracts of the talks presented during the seminar, as well as summaries of the discussion sessions. Seminar April 7-10, 2019 -http://www.dagstuhl.de/19152 2012 ACM Subject Classification Computer systems organization → Neural networks, Hardware → Biology-related information processing, Hardware → Hardware-software codesign License Creative Commons BY 3.0 Unported license © Hai Li The explosion of big data applications imposes severe challenges of data processing speed and scalability on traditional computer systems. However, the performance of von Neumann architecture is greatly hindered by the increasing performance gap between CPU and memory, motivating active research on new or alternative computing architectures. Neuromorphic computing systems, that refer to the computing architecture inspired by the working mechanism of human brains, have gained considerable attention. The human neocortex system naturally possesses a massively parallel architecture with closely coupled memory and computing as well as unique analog domain operations. By imitating this structure, neuromorphic computing systems are anticipated to be superior to conventional
doi:10.4230/dagrep.9.4.43 dblp:journals/dagstuhl-reports/ChakrabartyH0S19 fatcat:7fpavhm4gzgxnj2o23jm66sjiy