Traditional von Neumann architectures separate memory and processing, leading to the 'memory wall' and high energy costs for AI. Neuromorphic chips mimic the human brain, co-locating memory and compute.

Module 1: Spiking Neural Networks (SNNs)

Unlike standard ANNs that pass continuous float values, SNNs communicate via discrete, sparse 'spikes' over time. A neuron only computes when it receives a spike, drastically reducing power consumption—ideal for IoT and space applications.

Advantages of Neuromorphic AI

  • Extreme Energy Efficiency (orders of magnitude lower power than GPUs).
  • Low Latency Event-Driven processing.
  • In-memory computing bypasses the von Neumann bottleneck.