Neuromorphic Computing
- [Microelectronics: A simplified schematic of a crossbar circuit element designed for future low power, non-volatile memory or neuromorphic computing applications - US Department of Energy]
New Approaches to Computing Modeled on the Human Brain
- Overview
Neuromorphic computing is a brain-inspired approach to computing that mimics the structure and function of the human brain to process information more efficiently.
It involves designing both hardware and software that use artificial neurons and synapses to perform computations in a parallel, energy-efficient manner, which differs significantly from traditional, sequential computing.
This method is especially promising for applications that require real-time learning and low-power processing.
Please refer to the following for more information:
- Wikipedia: Neuromorphic Computing
- Types of Neuromorphic Computing
- Hardware-based: These systems use specialized chips to physically mimic neural structures, like spiking neural networks (SNNs), which are more similar to biological neurons.
- Software-based: These systems run models of neural networks on conventional hardware, but with algorithms designed to replicate the brain's function.
- Hybrid: Some systems integrate neuromorphic chips with traditional computers to boost performance for specific tasks.
- Neuromorphic Computing Function
- Parallel processing: Instead of processing data sequentially, neuromorphic systems perform many calculations simultaneously, similar to how the brain works.
- Energy efficiency: By integrating memory and processing, neuromorphic chips reduce the need to constantly transfer data, which drastically lowers energy consumption compared to traditional computers.
- Real-time learning: They have the potential to learn and adapt in real-time from new data without needing to be retrained on large datasets.
- Neuromorphic Computing Components
- Artificial neurons: The fundamental processing units in a neuromorphic chip that mimic biological neurons.
- Artificial synapses: The connections between artificial neurons that transmit signals, similar to biological synapses.
- Energy-efficient networks: The overall architecture is designed to be far more energy-efficient than conventional computer systems, which use significant power to perform complex tasks.
[More to come ...]
