RCN-SC: Research Coordination Network for Design and Testing of Neuromorphic Integrated Circuits

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The project is facilitating a Neuromorphic Integrated Circuits Education (NICE) research coordination network (RCN) that will lower the barrier of entry for students and researchers interested in the design, fabrication and testing of neuromorphic integrated circuits (ICs). As artificial intelligence (AI) continues to permeate society, there has been an ever-increasing demand for efficient AI hardware, accelerators and integrated circuits (ICs). The field of neuromorphic engineering has been instrumental in advancing AI hardware, with concepts like compute-in-memory, pooling, and attention being integrated into mainstream IC designs. However, to sustain the growth of the field, there is a pressing need to address the education and workforce gap in the discipline of IC design and fabrication. By leveraging the popularity of AI, machine learning and neuromorphic engineering disciplines, the project is motivating researchers to acquire design skills that can then be applied to various domains of IC design. At the centerpiece of this RCN is the Annual Telluride Neuromorphic Cognition Engineering (TNCE) workshop which is an influential and highly anticipated event in the field of neuroscience and artificial intelligence. This workshop brings together leading researchers, experts, and students from various disciplines to explore and advance our understanding of neuromorphic computing and cognition. As a part of this RCN, the project is leveraging the workshop infrastructure to organize discussion groups, facilitate hands-on training events, and form sustainable research cohorts around the theme of neuromorphic ICs.The intellectual merit of the RCN is to understand and exploit emergent neurodynamics and noise-induced processes in neuromorphic devices and hardware to efficiently solve specific computing tasks. To achieve this the RCN is utilizing the extensive expertise within the network to investigate novel neuromorphic architectures, circuits and hardware that could lead to significant performance advantages when compared to other computing architectures that use central processing units (CPUs), graphical processing units (GPUs) or quantum processors. The RCN consists of three coordination sub-networks: (a) The NICE-RCN Co-design Framework is concentrating on the development of behavioral models and software. These tools will enable the simulation and evaluation of different architectures on benchmark tasks; (b) The NICE-RCN IC Design Framework will focus on designing neuromorphic Process Design Kits (PDKs) using open-source IC fabrication tools accessible through Efabless, as well as the Taiwan Semiconductor Manufacturing Corporation (TSMC) PDK tools provided by Muse Semiconductors; (c) The NICE-RCN IC Testing Framework will establish a testing infrastructure and create benchmarks for evaluating the performance of neuromorphic ICs.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.