What is neuromorphic computing?
Neuromorphic computing is the next generation of AI that will extend it to learn and retain information and even make logical adaptations like a human brain. In this new method of computer engineering, hardware and software elements of a computer will be designed and engineered according to the same physics of computation used by the human nervous system and brain.
An artificial neural network program differs from neuromorphic computing as it runs under a normal computer that mimics the logic of how a human brain thinks. Neuromorphic computing will be ideal as a hardware version to run a neural network as a software version. A precise electric current has to flow across a synapse or the space between neurons, depending on the quantity and type of ion
Unlike traditional computers, where there are only two possible options, more computational options will be possible when the receiving computer neuron is activated in some way. The neuromorphic chips could be more energy efficient, especially for complex tasks, as it has the ability to transmit a gradient of understanding from neuron to neuron and have them all working together simultaneously.
The materials that are used in existing computers will not be suitable for realizing the exciting potential of neuromorphic chips. The current between artificial neurons cannot be controlled by something like silicon as its physical properties make it flow randomly all over the chip. A team at MIT has designed a neuromorphic chip by layering single crystalline silicon and silicon-germanium on top of one another. There will be an organized flow of ions when an electric field is applied to this device. The architecture of neuromorphic systems is advancing in a way that neurons on these chips learn as they compute.
What are the Applications of Neuromorphic Computing
|Challenges and Considerations
|Artificial Intelligence (AI)
|Neuromorphic computing enhances the efficiency and performance of AI algorithms by simulating neural networks more closely to the human brain’s architecture.
|Improved learning capabilities; Better mimicry of human cognition; Energy-efficient.
|Limited scalability; Complexity in hardware implementation; Training challenges for large-scale networks.
|Neural Prosthetics and Brain-Machine Interfaces
|Applied in the development of brain-machine interfaces (BMIs) and neural prosthetics, aiming to restore or enhance nervous system functionality.
|Direct interaction with neural circuits; Potential for enhanced control and feedback in prosthetic devices.
|Ethical concerns; Long-term biocompatibility; Limited understanding of neural codes.
|Contributes to advanced robotic systems, enabling more natural and adaptive behaviors, improved navigation, learning capabilities, and effective human interaction.
|Enhanced adaptability; Improved human-robot collaboration; Real-time decision-making.
|Hardware limitations for real-time processing; Safety concerns in complex environments; Ethical considerations.
|Used in computer vision, speech recognition, and natural language processing for efficient and real-time processing of sensory data.
|Improved safety, Efficient navigation in complex scenarios, and adaptive decision-making.
|Limited interpretability; Complexity in designing neuromorphic algorithms; Resource-intensive during training.
|Enhances perception and decision-making capabilities in autonomous vehicles, making them better equipped to handle complex and dynamic environments.
|Improved safety, Efficient navigation in complex scenarios, adaptive decision-making.
|Safety and regulatory challenges; Limited interpretability of neural networks; Integration with traditional systems.
|Applied in tasks such as reasoning, problem-solving, and decision-making, improving a system’s ability to understand and respond to complex scenarios.
|Enhanced reasoning abilities; Improved problem-solving; Adaptability to dynamic environments.
|Limited explainability; Difficulty in encoding human-like intuition; Ethical concerns in decision-making algorithms.
|Emphasizes energy efficiency, making neuromorphic systems suitable for applications like IoT devices and edge computing, where low power consumption is critical.
|Lower power consumption; Prolonged battery life in devices; Reduced environmental impact.
|Hardware development costs; Trade-offs between energy efficiency and computational power; Limited availability of neuromorphic hardware.
|Utilized in various fields, including image and speech recognition, for efficient identification and adaptation to patterns in data.
|Efficient pattern identification; Robust learning from data; Real-time pattern adaptation.
|Limited generalization to new patterns; Hardware constraints in large-scale deployments; Privacy concerns in pattern recognition applications.
Neuromorphic devices could be used in prosthetics and to improve drug delivery in the human body. Traditional prosthetic devices can be replaced with neuromorphic devices to create a seamless and realistic experience. Its highly responsive nature makes it capable of releasing a drug upon sensing a change in the human body. A computer that behaves like a human brain will have the computing power to simulate something as complicated as the brain, such as identifying diseases like Alzheimer’s.
Large Scale Operations
Neuromorphic computing can benefit large-scale projects by easily processing large sets of data from environmental sensors that could measure parameters like content, temperature, and radiation. It will be easier to reach effective conclusions as various patterns in the data can be recognized by the neuromorphic computing structure.
The building materials of neuromorphic computers can be transformed into easily manipulated fluids to be used in product customization. In liquid form, they can be used in additive manufacturing to create devices fit for specific needs.
The field of neuromorphic computing will push to match the functionality of the human brain which has neurons that are extremely fast and energy-efficient in receiving, processing, and sending signals. As the brain’s ability to collect and apply information is a particular focus in the field of AI, it would be beneficial for the two fields to collaborate going forward.
Researchers in the UK say that a system called SpiNNaker, can be used to simulate the behavior of the human cortex. SpiNNaker, which stands for Spiking Neural Network Architecture, designed by a team at the University of Manchester, is another leap in the performance of neuromorphic computing. The project took a different approach by using traditional digital parts like cores and routers that connect and communicate with each other in innovative ways. SpiNNaker has achieved a huge milestone in neuromorphic computing by matching the results with that of a traditional supercomputer. It is expected to achieve computing performance with higher speed and more complexity for less energy cost.
Artificial neural systems are created in neuromorphic computing by combining disciplines, including computer engineering, electronics engineering, biology, mathematics, and physics. The Loihi project by Intel and TrueNorth’s neurons by IBM are some of the exciting projects that aim to revolutionize the computing system inspired by the human brain. These projects focus on having a better grasp on the functioning of the human brain, mimicking biological systems using improved building materials, and optimizing neural algorithms with better hardware architectures.