The emerging new interest in artificial intelligence (AI) has led to a paradigm shift in the field of computer architecture. Architectures that focus on machine learning and neuromorphic computing are increasingly being adopted into the mainstream. The design of future processors is increasingly being predicated on metrics based on cognitive applications. The interplay between the development of state-of-the-art algorithms in cognitive sciences, computer vision, speech recognition and the design of the underlying system architectures has resulted in several cross-disciplinary advances. After highly successful editions of the Workshop on Cognitive Architectures in the past, in this year’s edition we expect to consolidate the many recent works in this field and provide concrete roadmaps as to what subsequent directions the research in this field should take. This workshop at HPCA proposes to bring together researchers and practitioners within systems architecture, computer vision, artificial intelligence and robotics to discuss the latest ideas, applications and commercialization strategies around the cognitive computing theme. In addition, it also aims to foster an interest in such emerging fields among industry and academic researchers alike. The primary focus is expected to be on driving towards a better understanding of key cognitive algorithms of interest and the allied architectural support issues.
Of particular interest are application areas of mobile cognition, which may comprise of a distributed swarm of intelligent computing modules. This paradigm is relevant in the context of unmanned aerial vehicles and connected cars. Further, the rising investment by industry in designing IoT (Internet-of-Things) platforms, has resulted in several opportunities to build architectures that support capabilities such as intelligent data exchange and interaction with cloud, pattern matching, data analytics and other cognitive aspects to build truly transformative systems.
Recent advances in architectures for AI have been instrumental in the development and deployment of autonomous systems, such as self-driving vehicles, and unmanned aerial vehicles. The Tesla FSD and Nvidia Xavier SoC are examples of commercial chips in the autonomous driving space with dedicated hardware units for processing AI algorithms. Next-generation processors in this domain are being envisioned as multi-agent systems that support swarm-based decision-making, as well as intelligent data exchange capabilities amongst each other or with a backing cloud server. The proliferation of mobile computing platforms, Internet-of-Things and cloud support features thereof have opened up exciting new opportunities for such real-time, mobile (distributed or swarm-driven) cognition. The CogArch workshop solicits formative ideas and new product offerings in this general space. Topics of interest include (but are not limited to):
Submitted manuscripts must be in English of up to 2 pages (with same
formatting guidelines as main
conference) indicating the type of submission: regular presentation or prototype
demonstration. Submissions should be sent to
email@example.com by December 6th, 2019.
If you have questions regarding submission, please contact us: firstname.lastname@example.org
CogArch will feature a session where researchers can showcase innovative prototype demonstrations or proof-of-concept designs in the cognitive architecture space. Examples of such demonstrations may include (but are not limited to):
To be announced
Vijay Janapa Reddi is an Associate Professor of Electrical Engineering at Harvard University. His research interests are in edge computing and IoT systems, specifically in the context of mobile devices and autonomous machines. He holds a Ph.D. degree from Harvard University.
Augusto Vega is a Research Staff Member at IBM T. J. Watson Research Center involved in research and development work in the areas of highly-reliable power-efficient embedded designs, cognitive systems and mobile computing. He holds a Ph.D. degree from Polytechnic University of Catalonia (UPC), Spain.
Karthik Swaminathan is a Research Staff Member at IBM T. J. Watson Research Center. His research interests include power-aware architectures, domain-specific accelerators and emerging device technologies in processor design. He is also interested in architectures for approximate and cognitive computing, particularly in aspects related to their reliability and energy efficiency. He holds a Ph.D. degree from Penn State University.
Nandhini Chandramoorthy is a Research Staff Member at IBM T. J. Watson Research Center involved in Deep Neural Network ASIC design with techniques to improve reliable operation at very low supply voltages, and pre-RTL performance modeling tools for multi-core architectures. She holds a Ph.D. degree from Penn State University.
Alper Buyuktosunoglu is a Research Staff Member at IBM T. J. Watson Research Center. He has been involved in research and development work in support of IBM Power Systems and IBM z Systems in the area of high performance, reliability and power-aware computer architectures. He holds a Ph.D. degree from University of Rochester.
Pradip Bose is a Distinguished Research Staff Member and manager of Efficient and Resilient Systems at IBM T. J. Watson Research Center. He has over thirty-three years of experience at IBM, and was a member of the pioneering RISC super scalar project at IBM (a pre-cursor to the first RS/6000 system product). He holds a Ph.D. degree from University of Illinois at Urbana-Champaign.