In this episode of Data Science at Home, we explore the fascinating world of neuromorphic computing — a brain-inspired approach to computation that could reshape the future of AI and robotics. The episode breaks down how neuromorphic systems differ from conventional AI architectures like transformers and LLMs, diving into spiking neural networks (SNNs), their benefits in energy efficiency and real-time processing, and their limitations in training and scalability. Real-world applications are highlighted, including low-power drones, hearing aids, and event-based cameras. Francesco closes with a vision of hybrid systems where neuromorphic chips and LLMs coexist, blending biological inspiration with modern AI.
📚 References
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SpikingJelly: https://github.com/fangwei123456/spikingjelly
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IBM TrueNorth: https://research.ibm.com/blog/brain-inspired-chip
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Intel Loihi 2: https://www.intel.com/content/www/us/en/research/neuromorphic-computing.html
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SpiNNaker: https://apt.cs.manchester.ac.uk/projects/SpiNNaker/
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BioRobotics Institute: https://www.santannapisa.it/en/institute/biorobotics
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