SHF: SMALL: Deep Spiking Neural Networks: Algorithms, Architecture, and Devices

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This research attempts to explore new computing models by mimicking mammalian brains via new learning methods for brain-like Spiking Neural Networks (SNNs). However, the inherent mismatch between the characteristics of the CMOS devices and the functionality of neuron and synapses in the brain lead to drastic increase in circuit complexity. Spintronics based (non-CMOS) devices will be used in this research for hardware design of such brain-like architectures. The nanoHUB (www.nanohub.org) facility at Purdue University will be used to disseminate research results and to make the devices-to-systems simulation framework for neuromorphic computing available to researchers, educators, and students. The PI will develop new course modules to include the concept of using spin as a state variable for computation. These will be further used to update undergraduate and graduate level courses at Purdue, and made available through the nanoHUB.Specifically, research on new spin-transfer torque devices will be exploited to investigate the possibility of mimicking functions of spiking neurons and synapses with efficient learning capabilities. The research will be driven by a combination of top-down and a bottom-up approach, where the team will explore algorithms that will be hardware compatible for implementing low-power on-chip learning mechanisms. The proposed research will develop new learning methods on brain-like spiking neural networks and use efficient device/circuit/algorithm co-design to achieve highly energy efficient spiking neural networks for image recognition and video analysis problems.