Project

Neuromorphic Computing 
 

Neuromorphic computing is a field of computer engineering that aims to develop computer systems modeled on the human brain, mimicking its biological architecture through the use of neural networks. These networks are composed of interconnected nodes, or "neurons," that process and transmit information using electrical signals.

One of the main advantages of neuromorphic computing is its ability to process data in a highly parallel manner, which can lead to significant improvements in speed and efficiency compared to traditional computing methods. In addition, neuromorphic computing systems are highly adaptable, and can learn and adapt to new tasks and environments in real time.

Photonic computing, which uses light instead of electricity to perform computations, has the potential to greatly enhance the capabilities of neuromorphic computing systems. light can transmit and process data at much faster speeds than electronic systems, enabling neural network operations to be performed more quickly and efficiently. Parallel computations are also more efficient, which is especially beneficial in the training of neural networks that require large amounts of computations to be performed simultaneously. Light-based computations require less energy than electronic computations, which can lead to significant improvements in power efficiency.

By drawing from silicon photonics (photonic computing using silicon as an optical medium) to build on the existing semiconductor industry, RESPITE aims to develop neumorphic computing models that have emerging applications in fields ranging from autonomous cars to image and data processing and telecommunications.