With the unprecedented success of deep learning in pattern recognition tasks, the demands for computational expenses required to train and implement such Artificial Intelligence (AI) systems have also grown beyond current capabilities. â€œNeuromorphic Computingâ€ strives to reduce the gap in computational efficiency of AI platforms by exploring bio-plausibility in the underlying computational primitives and hardware substrate. This project goes beyond the focus of current neuromorphic computing architectures on computational models for neuron and synapse to examine other computational units of the biological brain that might contribute to cognition and especially self-repair. To this end, this EAGER project will forge new directions by drawing inspiration and insights from computational neuroscience regarding functionalities of glial cells and explores their role in the fault-tolerant capacity of emerging hardware enabled neuromorphic computing platforms. Graduate students and undergraduates from Penn State's Schreyer Honors College will be involved in the project. The highly interdisciplinary nature of the project will contribute significantly to the training of next generation students who will gain knowledge in the design of neuromorphic computing frameworks combining knowledge from hardware, neuroscience and machine learning. The PI plans to integrate the results from this project into the Electrical Engineering departmental K-12 summer camp.
Prior literature on exploring impact of astrocytes on self-repair has been primarily confined to small scale networks without any machine learning perspective. Further, self-repair has been studied primarily from a simplistic software simulation standpoint with stuck-at-zero faults. Neuromorphic hardware implementations for astrocyte functionalities have been also limited to Complementary Metal Oxide Semiconductor (CMOS) technology â€“ which is highly energy and area inefficient due to the functional mismatch between CMOS transistors and glial functionality. To bridge this gap, the proposed research agenda explores a hardware-software co-design approach to incorporate glial cell functionality in neuromorphic platforms through the usage of spintronic technologies. The EAGER program focuses on the following research thrusts: (i) Exploiting astrocyte computational models to evaluate the aspects of glial functionality crucial for self-repair in the context of neuromorphic machine learning platforms, (ii) Exploring spintronic device and circuit primitives to design a coupled neuron-synapse-astrocyte network capable of self-repair where the underlying device mimics the astrocyte functionality through their intrinsic physics, and (iii) Combination of the above top-down and bottom-up perspectives to leverage astrocyte self-repair in the context of hardware realistic faults like resistance drift, parasitic effects, device to device variations, among others in neuromorphic AI systems. The proposed research agenda, would provide proof-of-concept results toward the development of a new generation of efficient neuromorphic platforms that are able to autonomously self-repair non-ideal hardware operation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.