![]() ![]() Techniques based on wavelength division multiplexing (WDM) would allow each processor to connect to hundreds of others simultaneously without the corresponding degradation in speed typically observed in electronics. ![]() Our laser model exhibits the same behavior as the 'Leaky Integrate-and-Fire model' on time scales that are millions of times faster. Lasers (orange layer) are connected together via photonic waveguides (blue and gray) while electronic circuitry (yellow) controls stability, self-healing, and learning. (Right) An illustration of what a large-scale chip might look like. Thousands of devices could potentially be printed onto a single chip. The device occupies an area that is a fraction of a square millimeter. (Left) An illustration of a hypothetical 'laser neuron' that emulates the key features of biological spiking neurons. Top right: Excitatory and inhibitory dynamics in multi-channels.īottom: Architecture of Photonic Spiking Neural Networks. Excitatory and inhibitory inputs are incident on a balanced photodetector (PD) pair, which drives a distributed feedback (DFB) laser biased with current Ip. Top left: Topographic micrograph of an integrated laser neuron. It is able to emulate the key properties of the leaky integrate-and-fire (LIF) neuron model and exhibits many favorable features for scalability, energy efficiency, and high-speed spike processing. We study the first results of a novel device, an excitable laser on a PIC platform. This project is about understanding and building photonic spiking neural networks with a particular focus on photonic integrated circuits. Neuromorphic photonics is an emerging field at the intersection of photonics and neuromorphic engineering, with the goal of producing accelerated processors that combines the information processing capacity of neuromorphic processing architectures and the speed and bandwidth of photonics. ![]()
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