A new type of brain-like computer was created using a magnetic material, marking a significant step forward in neuromorphic computing. The new magnetic brain-like computer can copy how neurons and synapses work in the human brain. It is energy-efficient, flexible, and very reliable. Scientists and engineers will have to design AI and computers differently due to this artificial neural network.
The Concept of Brain-Like Computing with Magnets
Because traditional computers keep memory and processing tasks separate, data must be moved back and forth between memory chips and the processing unit, which requires a significant amount of time and power. Neuromorphic computation, which is inspired by the brain, attempts to solve this problem by integrating memory and computation within the same device. It does this by copying how the brain collects and processes information through many connected nerve units and bends.
This technology is built around raising the capacities of magnetic tunnel junctions and, by extension, spintronic devices. An MTJ is a combination of two magnetic terminations separated by an insulation sheet. The orientation of the two layers in the MTJ determines the movement of electrons through it, differentiating between two levels of resistance and serving as binary switches. However, implemented MTJs may also spontaneously switch between levels in response to voltage signals. This mechanism enabled the device to imitate the controlled randomness that occurs among neurons in a living brain.
How Magnetic Devices Imitate Brain Function
Moreover, these magnetic components can mimic the essential behaviours of neurons, such as the “leaky-integrate-and-fire” model, where the sum of incoming signals grows and then resets when it reaches the threshold at which the output impulse is generated. It enables the creation of artificial neurons that can sum signals over time and generate stable, reliable results.
“And researchers have demonstrated networks of these magnetic neurons — not only capable of learning and adapting through experience in the world, as our brains do, but not solely through massive labeled datasets and programmed instructions alone — with an obsessive devotion to the letters L and T — not only outperform existing neuromorphic systems but so much more energy-efficient that you can hardly see the trace behind a fine point-laser cutting-paper decoy.”
Advantages of Magnetic Brain-Like Computers
The extraordinary energy efficiency of the technology constitutes one of the most significant benefits of the invention. Indeed, by conducting storage and computation within the magnetic memory elements, the novel computers eliminate the majority of energy wasted in moving the data between these two subsystems. Particularly, the researchers believe that such processors can carry out more than 600 trillion operations in a second with a watt-equivalent, exceeding existing next-generation memory by more than a factor of six and surpassing GPUs that power present AI by a factor of thousands.
Additionally, magnetic parts are more reliable than memristors or phase-change memories, which are typically used in neuromorphic computing. They don’t have as many problems with resistance drift or random errors, which means they last longer and work more consistently.
Potential Applications and Future Prospects
…a new possibility of embedding AI intelligence directly into smart devices that run on little energy. Use-cases conceptualized by researchers that could be realized using low-power, in-memory computing include autonomous robots with the capacity to learn from their environment, wearables that adjust to users in real-time, and sensors for medical monitoring that instantly perceive changes in health. (Devices, 2018). Another key new technology is edge computing devices, which do not require constant online connectivity to be useful.
Also, since these MTJ arrays can be made using standard semiconductor methods, it may soon be possible to integrate millions of magnetic synapses with existing circuitry commercially. This would bring brain-like computing into everyday technology.
Conclusion
Creating brain-like computers using magnets marks a significant advance toward energy-efficient, adaptive, and robust artificial intelligence systems that closely mimic the workings of the human brain. Without the use of magnets, it would have been impossible to replicate neural computation and memory using the physical properties of magnetic materials. Future generations of smart, low-power computing technologies will be significantly impacted by this, spanning from robotics and healthcare to AI-based consumer technology.