Research

Trainable Reconfigurable Development Platform for Large-Scale Neuromorphic Cognitive Computing

Gert Cauwenberghs gert@ucsd.edu (Principal Investigator), Amitava Majumdar (Co-Principal Investigator), Emre Neftci (Co-Principal Investigator) - July 3, 2018

Neuromorphic cognitive computing aims at learning to solve complex cognitive tasks by emulating the principles and physical organization of highly efficient and resilient adaptive information processing in the biological brain. Despite over 30 years of development and a recent surge of broad interest across all Science, Technology, Engineering and Mathematics (STEM) disciplines, access to neuromorphic cognitive computing remains mostly limited to a small community of highly trained researchers in the field due to high entry barriers and costs associated with the specialized nature and complex operation of currently available systems. This project will construct and support a general-purpose neuromorphic cognitive computing platform that will be the largest and most versatile realized to date as well as the first to be broadly available and open to the research community at large, for research into new forms of brain-inspired computing that are more effective and more efficient in approaching the cognitive capabilities of the human mind. Targeting wide adoption by a diverse cross-section of users in the broader STEM research community, the platform will feature a natural user interface that shields novice users from the challenges arising in operating and configuring highly specialized neuromorphic hardware, by providing a set of user-friendly software tools maintained by and shared with the user community. Building on extensive existing network and storage infrastructure for user access and data sharing at the San Diego Supercomputer Center, the platform will be hosted and maintained through the Neuroscience Gateway (NSG) Portal, which currently serves over 600 active users in the scientific community.

The large-scale neuromorphic platform will serve as a new and unparalleled resource to the Computer and Information Science and Engineering (CISE) research community, addressing a great need for an experimental testbed for research in alternative forms of computing beyond the traditional von Neumann paradigm and the impending physical limits to Moore's Law expansion in the scaling of computing technology. The reconfigurable platform will feature a hierarchically interconnected network of in-memory computing processing nodes that emulates, in real-time, highly flexible neural dynamics (integrate-and-fire, graded, stochastic binary, etc) of up to 128 million neurons with high flexible connectivity and plasticity (spike-timing dependent plasticity, gradient-based deep learning, etc) of up to 32 billion synapses. The system will be capable of biophysical detail in computational neuroscience modeling, as well as high performance and efficiency in on-line adaptive pattern recognition, serving and bringing together both computational neuroscience and computational intelligence communities that have traditionally pursued disparate computational approaches. The user interface of the platform will support software tools and resources for deep learning and run-time optimization in artificial intelligence applications, and for interference of structure and functional connectivity from recorded neural activity in computational neuroscience research, among others. To facilitate greatest scientific and societal impact, the infrastructure will be made available free of charge, on a time-managed shared basis, to any researcher in return for agreeing to share source code and data necessary to replicate results reported in the literature.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Pedroni, Bruno U. and Joshi, Siddharth and Deiss, Stephen R. and Sheik, Sadique and Detorakis, Georgios and Paul, Somnath and Augustine, Charles and Neftci, Emre O. and Cauwenberghs, Gert. "Memory-Efficient Synaptic Connectivity for Spike-Timing- Dependent Plasticity," Frontiers in Neuroscience, v.13, 2019. doi:10.3389/fnins.2019.00357  

Detorakis, Georgios and Sheik, Sadique and Augustine, Charles and Paul, Somnath and Pedroni, Bruno U. and Dutt, Nikil and Krichmar, Jeffrey and Cauwenberghs, Gert and Neftci, Emre. "Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning," Frontiers in Neuroscience, v.12, 2018. doi:10.3389/fnins.2018.00583