EFRI BRAID: Rapid contextual learning in resilient autonomous systems

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Neuromorphic computing seeks to identify key operational principles of the brain and implement them in artificial computing systems. The effort comprises both new hardware platforms with architectures based on decentralized brain networks (such as the Intel Loihi and IBM TrueNorth platforms) and the emerging computational algorithms that are required to run this new hardware effectively. In hardware, the diagnostic principles of this new computing paradigm are parallel, asynchronous local computation and the colocalization of memory and compute resources. This means that thousands of small processors all operate separately – without using a common clock, a shared memory store, or any other common resources that would slow the whole system down to the speed of its slowest component. The result is a computer system that can perform many types of tasks much faster, and with much lower energy expenditure, but that requires a complete rethinking of software algorithms in order to perform real-world tasks effectively using these fundamentally decentralized circuits. In the present application, computational principles extracted from biological brain circuits are employed to develop such working algorithms, and also to identify and analyze core computational motifs from these algorithms for future repurposing. Additional principles drawn from neuroscience also will be implemented and assessed, particularly local complexity and heterogeneity, in which the “neurons” can be individually complex and very different from one another, and adaptive network expansion, in which the network itself can grow in accordance with its acquired learning and expertise. Training and exposure to these transformative compute strategies will be broadened via multiple initiatives at Cornell and Georgia Tech, ranging from historically successful diversity, equity, and inclusion strategies to K-12 partnerships to immersive STEM teaching facilities and outreach programs. The potential advantages of neuromorphic computing platforms are both clear and profound, but also are limited by the paucity of well-developed neuromorphic algorithms capable of leveraging these advantages to address real-world problems. The Sapinet network, based on computational principles extracted from the biological olfactory system, shows promise as a neuromorphic algorithm for signal restoration and identification under noise. Using an explicit theoretical roadmap, this network architecture will be developed to incorporate additional brain-inspired strategies for resilient and robust autonomy, such as context dependence, multimodal integration, rich category learning, and explicit representations of similarity that together promise to enable superior and more sophisticated performance. Second, owing in part to the heterogeneity of design elements that underlie its power, neuromorphic computing presently is limited by a paucity of formal analysis and optimization techniques. A set of computational motifs (“numerical recipes”) and analysis strategies for neuromorphic operations will be developed, in service to future applications that may lack an explicit parallel in systems neuroscience. Finally, the resulting intelligent systems will be instantiated in software and in neuromorphic hardware, and ultimately in prototype devices for real-world deployment and testing. The overall goal is to construct and deploy locally intelligent, energy-efficient, and portable edge devices capable of a high degree of performance autonomy; i.e., that exhibit resilient and context-aware task performance under suboptimal and unpredictable real-world conditions.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.