Neuromorphic Computing Architecture Design Princip

# Neuromorphic Computing Architecture Design Principles

## Core Concepts

Neuromorphic computing aims to mimic the structure and function of the biological brain to achieve efficient and robust computation. Unlike traditional von Neumann architectures, which separate processing and memory, neuromorphic systems integrate these functions, enabling parallel and event-driven processing.

### Key Principles:

*   **Parallelism:** Massive parallel processing, similar to the brain's billions of neurons.
*   **Event-Driven Computation:** Processing occurs only when there's a significant change in input (spikes), reducing energy consumption.
*   **Sparsity:** Biological neural networks are sparse; only a small fraction of neurons are active at any given time.
*   **Locality:** Computation is performed locally, minimizing communication overhead.