Neuromorphic chips are a type of specialised hardware designed to mimic the structure and functioning of the human brain.
It is also known as neuromorphic computing or neuromorphic hardware. So, in simpler terms, neuromorphic computing is a method of computer engineering in which elements of a computer are modelled after systems in the human brain and nervous system.
These chips are a part of the broader field of neuromorphic engineering, which draws inspiration from neuroscience to develop more efficient and brain-like computational systems.
How do they work?
Neuromorphic chips or computing uses hardware based on the structures, processes and capacities of neurons and synapses in biological brains.
This means that all these determine the hardware. The most common form of neuromorphic hardware is the spiking neural network (SNN). In this hardware, spiking neurons, otherwise called nodes, process and hold data like biological neurons.
Artificial synaptic devices then connect spiking neurons. They use analogue circuitry to transfer electrical signals that mimic or imitate brain signals. As a result, instead of encoding data through a binary system like most standard computers, spiking neurons measure and encode the discrete analogue signal changes themselves.
Neuromorphic computing can be used for deep learning applications; next-generation semiconductors; transistors; accelerators; and autonomous systems, e.g. robotics, drones, self-driving cars and artificial intelligence (AI).
Here are some key aspects of neuromorphic chips that are worthy of note:
- Biological Inspiration: Neuromorphic chips are inspired by the structure and operation of biological neural networks, such as the human brain. They aim to replicate the parallel processing, low-power consumption, and adaptability of biological neurons and synapses.
- Neurons and Synapses: In neuromorphic chips, artificial neurons and synapses are created using electronic components like transistors. Neurons receive and process information, while synapses are the connections that transmit signals between neurons.
- Parallel Processing: One of the key advantages of neuromorphic chips is their ability to perform parallel processing. Unlike traditional von Neumann architecture, where processing and memory are separate, neuromorphic chips often integrate memory and processing, allowing for efficient parallel computation.
- Low Power Consumption: Neuromorphic chips are designed to be highly energy-efficient, mimicking the brain’s ability to perform complex tasks with relatively low power consumption. This makes them suitable for applications where power efficiency is critical, such as edge computing and IoT devices.
- Adaptability and Learning: Neuromorphic chips are capable of learning from data and adapting to new tasks, making them suitable for machine learning and artificial intelligence applications. They can implement various learning algorithms, including unsupervised, supervised, and reinforcement learning.
- Sensor Integration: Neuromorphic chips are often used in sensor-rich environments, where they can process sensory data in real-time. This is particularly useful in applications such as robotics, autonomous vehicles, and environmental monitoring.
- Applications: Neuromorphic chips have a wide range of potential applications, including but not limited to image and speech recognition, natural language processing, pattern recognition, robotics, autonomous systems, and brain-computer interfaces.
- Challenges: Despite their promise, neuromorphic chips also face challenges, such as the need for specialised hardware and software, scalability issues, and the complexity of mimicking the brain’s intricate architecture. Researchers are actively working to address these challenges.
Neuromorphic computing promises to improve efficiency as it has gained attention from major chip manufacturers, such as IBM and Intel, as well as the United States military. Developments in neuromorphic technology could improve the learning capabilities of state-of-the-art autonomous devices, such as driverless cars and drones.
Neuromorphic computing is essential to the future of AI and as such, should be handled meticulously with much care.
Researched by: Aduloju Oluwatoyosi Afolabi
