17th ACM International Conference on Computing Frontiers (CF'20)

1-10 June, 2020, Catania, Sicily, Italy
www.computingfrontiers.org

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Key Dates Full Papers

Paper Submission Deadline

January 28, 2020

Author Notification

March 15, 2020

Final Papers Due

April 5, 2020

Computing Frontiers 2020 is proud to announce the following keynote speakers and talks this year:

• Keynote 1: Shih-Chii Liu
  Exploiting sparsity in event-driven deep neural network computing architectures
• Keynote 2: Steve Oberlin
  HPC + AI: The Future Of High Performance Computing in a Post-Moore’s Law World

Exploiting sparsity in event-driven deep neural network computing architectures

Professor Shih-Chii Liu, Institute of Neuroinformatics, University of Zurich and ETH Zurich, CH

Bio:
Shih-Chii Liu is a professor in the Faculty of Science at the University of Zurich. She co-directs the Sensors group (http://sensors.ini.uzh.ch) at the Institute of Neuroinformatics, University of Zurich and ETH Zurich. Her research focus is on the design of low-power neuromorphic asynchronous spiking auditory and vision sensors, bio-inspired computing circuits, and more recently on event-driven deep neural network processors and their use in neuromorphic artificial intelligent systems. Dr. Liu is past Chair of the IEEE CAS Sensory Systems and Neural Systems and Applications Technical Committees. She is current Chair of the IEEE Swiss CAS/ED Society and general co-chair of the 2020 IEEE Artificial Intelligence for Circuits and Systems conference.

Abstract:
A fundamental organizing principle of brain computing enabling its amazing combination of intelligence, quick responsiveness, and low power consumption is its use of sparse spiking activity to drive computation. Recent progress in the development of higher-performance event-driven deep networks, neuromorphic spike-event-based visual (DVS/ATIS/DAVIS) and auditory (AER-EAR/DAS) sensors along with versatile hardware such as FPGAs have stimulated have stimulated exploration of real-time sensor processing for wearable and IoT platforms. These systems enable "always-on" low-latency system-level response time at lower power than equivalent conventional solutions. I will show our recent work in constructing energy-efficient event-driven deep networks that exploit spatial and temporal sparsity and real-world examples of the use of these networks with these neuromorphic sensors.

HPC + AI: The Future Of High Performance Computing in a Post-Moore’s Law World

Steve Oberlin, CTO of Tesla at NVIDIA

Abstract:
Most AI researchers and industry pioneers agree that the wide availability and low cost of highly-efficient and powerful GPUs and accelerated computing parallel programming tools (originally developed to benefit HPC applications) catalyzed the modern revolution in AI/deep learning. Clearly, AI has benefited greatly from HPC. Now, AI methods, tools, and technology are starting to be applied to HPC applications to great effect. This talk will explore the potential of AI-driven compute architecture optimizations such as tensor processing units and multi-precision computing to accelerate scientific simulation, and describe an entirely new HPC workflow that uses traditional numeric simulation codes to generate synthetic data sets to train machine learning algorithms, then employs the resulting AI models to predict the computed results, often with dramatic gains in efficiency, performance, and even accuracy. Some compelling success stories will be shared, and the implications of this new HPC + AI workflow on HPC applications and system architecture in a post-Moore’s Law world considered.