AI in Neuromorphic computing
AI in Neuromorphic computing is a fascinating and emerging field that aims to mimic the structure and function of the human brain using artificial neural networks. Neuromorphic computing is not only a way to enhance the performance and efficiency of AI systems, but also a way to explore the fundamental principles of cognition and intelligence.
In this post, I will introduce some of the key concepts and challenges of AI in Neuromorphic computing, as well as some of the current and future applications. I will also share some of the resources and tools that can help you learn more about this exciting topic.
What is AI in Neuromorphic computing?
AI in Neuromorphic computing is the use of artificial neural networks that are inspired by the biological neural networks of the brain. Unlike traditional AI systems that rely on von Neumann architectures, which separate computation and memory, neuromorphic systems integrate both functions in a distributed and parallel manner. This allows them to process large amounts of data with low power consumption and high speed.
Neuromorphic systems can also adapt to changing environments and learn from experience, just like the brain. They can perform tasks such as pattern recognition, classification, prediction, optimization, and decision making, among others.
Neuromorphic systems are composed of two main components: neuromorphic hardware and neuromorphic software. Neuromorphic hardware refers to the physical devices that implement the artificial neural networks, such as chips, sensors, or robots. Neuromorphic software refers to the algorithms and models that define the behavior and functionality of the neuromorphic hardware.
Some examples of neuromorphic hardware are:
- SpiNNaker: A massively parallel computer system that simulates up to one billion spiking neurons using 1 million ARM processors.
- Loihi: A neuromorphic chip developed by Intel that can simulate up to 130,000 neurons and 130 million synapses using 128 cores.
- BrainScaleS: A neuromorphic system that uses analog circuits to emulate the dynamics of biological neurons and synapses at 10,000 times faster than real time.
- TrueNorth: A neuromorphic chip developed by IBM that can simulate up to 1 million neurons and 256 million synapses using 4096 cores.
Some examples of neuromorphic software are:
- Nengo: A Python framework that allows users to build and simulate large-scale neural models on various platforms, including neuromorphic hardware.
- PyNN: A Python library that provides a common interface for writing and running neural network simulations on different simulators and hardware platforms.
- Brian: A Python library that allows users to write spiking neural network models using mathematical equations.
- GeNN: A C++ library that generates code for GPU-accelerated simulations of spiking neural networks.