Skip to content

Neuromorphic Computing

  • Biological inspiration: spiking neurons, synaptic plasticity, temporal coding
  • Spiking neural networks (SNNs): integrate-and-fire models (LIF, IF), spike timing
  • Learning in SNNs: STDP (spike-timing-dependent plasticity), surrogate gradient methods, conversion from ANNs
  • Neuromorphic hardware: Intel Loihi 2, IBM TrueNorth, SpiNNaker, BrainScaleS
  • Event-driven computation: asynchronous processing, energy efficiency
  • Event cameras (DVS): neuromorphic vision sensors, sparse temporal data
  • Applications: low-power edge inference, robotics, always-on sensing
  • Comparison with conventional deep learning: latency, power, accuracy tradeoffs