As traditional semiconductors reach the limits of miniaturization and capacity, a new approach to semiconductor design is needed. One promising alternative is neuromorphic computing. In contemporary CMOS architectures, the electronics that store the data are separated from those that process the information. So computers communicate by retrieving data from memory, moving it to the processing unit, and then moving it back to the memory. This back and forth is time-consuming, energy-consuming, and creates a bottleneck when large datasets need to be processed. Neuromorphic computing eliminates this back-and-forth with in-memory computing. And it relies on algorithms and networks to mimic the physics of the human brain and nervous system by establishing “spiking neural networks,” where spikes from individual neurons activate other neurons down a cascading chain. This allows neuromorphic chips to compute more flexibly and broadly, as its spiking neurons operate without any prescribed order.
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Inspired by recent findings on how oscillations and waves in the brain might coordinate and distribute computation among sub-networks, this work focuses on the sub-network level and (1) develop algorithms and learning rules for flexible computation and self-organized reconfiguration of neuronal circuits based on coupled oscillator networks and (2) implement those dynamically reconfiguring neural networks (DRNNs) on energy-efficient superconducting devices based hardware systems. The resulting flexible neuromorphic computing system will have a broad range of applications, including complex contextual and adaptive processing, attention-guided computation, belief propagation-based inference, and coordinating computation in network-of-experts networks. Contact: Dilip Vasudevan
The ExPlor Center aims to understand and realize a biologically-plausible neuromorphic system that is hierarchical, scalable, intelligent, efficient, and high-throughput by (a) taking the ‘best-of-both-worlds’ of electronics/photonics and utilizing bio-inspired intelligent materials, (b) combining dynamic synaptic plasticity and dendrite computing for brain-derived learning algorithms at multiple spatio-temporal scales, (c) enabling adaptive self-reconfiguration for different environments and applications, and (d) exploiting intelligent ionic and photonic materials on the silicon ecosystem for a new generation of neuromorphic computing. Contact: Kristofer Bouchard
