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Spiking neural networks (SNNs) are artificial neural network models that more closely mimic biological neural networks. In addition to neuronal and synaptic state, SNNs incorporate the variant time scale into their computational model. Since each neuron in these networks is connected to thousands of others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, very low communication latency is also needed. The 2D-NoC was used as a solution to provide a scalable interconnection fabric in large-scale parallel SNN systems. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. The combination of these two emerging technologies provides a new horizon for IC designs to satisfy the high requirements of low power and small footprint in emerging AI applications...more
Research about an event-driven scalable neuromorphic system for edge computing.
| Date | Task |
| January 1st- March 31 2020 | Running Demos and study of SW/HW tools |
| April 1st - May 22, 2020 | SNN Learning Algorithms Survey |
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