Neuromorphic Chip Architecture And Memristor Cross

# Neuromorphic Chip Architecture And Memristor Cross

## Core Concepts

Neuromorphic computing aims to mimic the structure and function of the biological brain to achieve energy-efficient and parallel processing. Unlike traditional von Neumann architectures, which separate processing and memory, neuromorphic systems integrate these functions, enabling in-memory computing.  A key component enabling this is the memristor.

**Memristors:** These are passive two-terminal devices whose resistance depends on the history of current flow.  They exhibit non-volatile memory characteristics, meaning they retain their resistance state even without power. This property makes them ideal for emulating synapses in neural networks.

**Spiking Neural Networks (SNNs):**  Neuromorphic chips often implement SNNs, which more closely resemble biological neurons than traditional Artificial Neural Networks (ANNs). SNNs communicate using discrete spikes (events) rather than continuous values, leading to event-driven, sparse computation and significant energy savings.