Algorithms for Spiking Neural Networks and Loihi

One of crucial aspects of information processing in our brain is the generation and transmission of action potentials, a.k.a. spikes. Spiking Neural Networks (SNNs) are the next generation Neural Networks which employ spiking neurons to accomplish general AI tasks. Unlike the Artificial Neural Networks (ANNs), they are inherently temporal in nature, with few works advocating SNNs to be more robust and potentially more powerful than ANNs!

So far, in our attempts to develop brain-like AI, we have been working with the conventional ANNs for long (since the start of 1940s), which are composed of highly abstracted out non-spiking neurons. The recent breakthroughs in AI can be largely attributed to the coupling of effective ANN training techniques and suitable hardwares e.g. GPUs/TPUs. But this has come at the cost of high energy consumption while training and inferencing; this is not at all scalable for edge devices or battery powered critical AI systems. SNNs on the other hand, in conjunction with specialized neuromorphic hardware e.g. SpiNNaker, Loihi, TrueNorth, etc. offer the promise of low-power and low-latency AI!

In this lab, we actively work in the field of Neuromorphic Computing to develop spiking network algorithms with a focus on their deployability on specialized neuromorphic hardware, e.g., Intel’s Loihi, Synsense's Speck and Xylo. Along with these neuromorphic boards, we also have DVXplorer's Micro DVS camera (640x480). We frequently collaborate with other (hardware) teams in this lab to develop novel neuromorphic-hardware-customized spiking networks for applications in wireless communication domain, apart from solving the general AI tasks including time series processing, event-based computer vision, etc.

Currently Involved Students

• Ramashish Gaurav
• Souvik Pramanik
• Alberta Dadeboe
• Ruizhe Li
• Zipeng Lin
• Daniel Rosen
• Meizi Song
• Dr. Chenyue Wang
• Mack Werner