EFRI BRAID: Scalable-Learning Neuromorphics

Recent advances in the field of artificial intelligence suggest that the route toward higher-intelligence brain-inspired systems is via significantly increasing network size. However, such a rather evolutionary approach faces dire challenges. Training the largest machine learning models for natural language processing requires months of data-center-scale computations, in other words, enormous energy, time, and cost. Reserves for improvements, e.g., due to the refinement of algorithms and hardware, seem limited, and most importantly, further advances could no longer be fueled by semiconductor technology scaling. Additionally, state-of-the-art models rely on offline training with vast amount of training data, i.e., are not capable of continual real-time learning. Such challenges naturally bring more attention to the biological neural networks, which are living proof of superior, agile, and adaptive intelligence running on very energy-efficient “brain” hardware. Exciting opportunities are presented by recent developments in the theory of spiking neural networks, the most biologically plausible models, and neuromorphic circuits implemented with dense emerging memory devices. The proposed project aims to capitalize on these advances and address the most pressing challenges to develop scalable algorithms and hardware for human-brain-scale neuromorphic systems with practically useful (robust, fast, inexpensive) learning capabilities. The project will enable neuromorphic systems of immediate importance for many practical applications, including autonomous robots and vehicles, and biomedicine, including portable and personal medical devices. Furthermore, the broad algorithm-to-system nature of the proposed research provides attractive opportunities for high-school, undergraduate, and graduate students to explore novel research and get exposed to the emerging field of neuromorphic computing. The proposed project will pursue outreach activities by leveraging programs at participating universities, with a particular focus on attracting minority students. The key features of our research are hardware-friendly local learning algorithms, a framework for continual online “one-shot” learning, and variation-tolerant in-memory computing hardware circuits. Specifically, on the algorithmic front, we focus on recurrent spiking neural networks with biologically-plausible spike frequency adaptation neurons. We will build on local learning algorithms recently proposed by our team members that facilitate learning over longer time scales via synaptic plasticity. These algorithms will be further extended to support continual learning and co-optimized with the hardware using neural architecture search techniques. On the hardware front, the focus is on hybrid neuromorphic circuits that take advantage of analog in-memory computing. Critical hardware challenges, such as the scaling of network complexity and implementation of robust in-situ learning, will be addressed by utilizing ultra-high-density crosspoint devices implementing fixed-value weights of the learning algorithms and novel variation-tolerant memristive synapses featuring both short-term and long-term plasticity. Algorithms and hardware circuits will be holistically integrated into the learning-to-learn spiking neural network framework. Such a framework enables two kinds of learning – a slow incremental one mimicking developmental learning and fast “one-shot” learning utilizing network dynamics. 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.