The field of Neuromorphic Computing, often referred to as brain-like computing, has been making significant strides recently. The latest leap forward comes from a team of scientists at the Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), who have developed an Artificial Synapse for Neuromorphic Computing. Using a semiconducting material known as scandium nitride (ScN), they have been able to develop devices that can mimic the operations of the human brain.
Artificial Synapse and its Significance
Neuromorphic hardware like that developed by the team at JNCASR is based on the idea of mimicking a biological synapse, which monitors and remembers signals generated by stimuli. ScN has been used in the creation of a device that not only controls signal transmission but also has the ability to remember the signal it transmits.
This development is significant as it presents a new material that can provide stable, CMOS-compatible optoelectronic synaptic functionalities at a lower energy cost. This kind of device has the potential to be translated into an industrial product. Traditional computers have separate processing and memory storage units which require significant energy and time to transfer data. On the contrary, the human brain, being a superior biological computer, is more efficient due to the presence of a synapse that functions as both a processor and a memory storage unit. This approach to brain-like computing could potentially meet rising computational demands in the current era of artificial intelligence.
Understanding Neuromorphic Computing
Rooted in the workings of the human brain and nervous system, the concept of Neuromorphic Computing emerged in the 1980s. This form of computing refers to designing computers that mimic the systems found in the human brain. One key advantage of Neuromorphic Computing devices is their relative efficiency — they can operate at the same level as the human brain, without needing a significant amount of space for software installations.
Among the many technological advancements that have rekindled interest in Neuromorphic Computing is the development of the Artificial Neural Network model (ANN).
Working Mechanism of Neuromorphic Computing
Neuromorphic computing operates through the use of Artificial Neural Networks (ANN). These networks consist of millions of artificial neurons, similar to those found within the human brain. These neurons pass signals to each other in layers, converting input into output through electric spikes or signals, based on the architecture of Spiking Neural Networks (SNN).
This process allows the computers to mimic the neuro-biological networks present in the human brain, effectively performing tasks such as visual recognition and data interpretation.
Impact of Neuromorphic Computing on Technology
Neuromorphic computing has opened doors for better technology and rapid growth in computer engineering. It has been a revolutionary concept within the realm of Artificial Intelligence. With the help of machine learning, one of AI’s key techniques, Neuromorphic Computing has greatly advanced the process of information processing, enabling computers to work with larger and more advanced technology.
The field of Neuromorphic Computing continues to offer promising developments that could significantly enhance the capabilities of artificial intelligence and conventional computing.
