The Quantum Insider’s Post

Scientists have developed a groundbreaking photonic chip that performs all key deep neural network computations using light! The chip achieves 92% accuracy in under half a nanosecond, matching traditional hardware while being incredibly energy-efficient. This could revolutionize high-speed applications and creates exciting possibilities. #AI #Innovation #DeepLearning #PhotonicComputing https://lnkd.in/emHtH-fJ

Polarization and wavelength routers based on diffractive neural network. Schematic diagram of implemented three routers using D2NN. The concept of deep diffractive neural networks (D2NN) was introduced by Professor Ozcan's research team in 2018. D2NN combines the principles of light diffraction with the functionalities of neural networks. It consists of a series of continuous diffractive layers. As light passes through these layers, variations in transmission or reflection coefficients at different positions cause changes in the light's phase and amplitude. Essentially, D2NN uses specially designed surfaces to precisely control light characteristics and output positions, enabling the processing of optical signals with exceptional speed and efficiency. This innovative approach opens new possibilities for optical computing and communication. Interest in the applications of D2NN. They have utilized this technology to develop three types of all-optical routers, which can guide light based on its wavelength and polarization, achieving efficient optical signal control. Their study, "Polarization and wavelength routers based on diffractive neural network," The first router developed is a polarization router, capable of routing two orthogonally polarized light beams. The wavelength router can route light of three wavelengths (1550, 1300, and 1100 nm) with excellent polarization maintenance. The polarization-wavelength composite router, by integrating wavelength and polarization degrees of freedom, is capable of handling six types of input light, thereby enhancing the information processing capability of the device. These devices' compact and passive design makes them ideal for integration into future optical devices. These advancements can lead to faster internet speeds and more reliable communication networks. The researchers predict that expanding the wavelength channels can further increase information processing capacity. #Polarization #wavelength #neuralnetwork #D2NN #opticalrouters #opticalcomputing #communication #transmission #reflection #amplitude

Deep neural network on a chip: Matrix multiplication with light+electronics and optics to implement nonlinear operations.

Photonic processor could enable ultrafast AI computations with extreme energy efficiency This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time. MIT News https://lnkd.in/eCUpumd9

MIT News writes "Photonic processor could enable ultrafast AI computations with extreme energy efficiency - This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time." https://lnkd.in/ekFSrKQ6. #artficialintelligence #photonicprocessors #deepneuralnetwork #realtime #mitnews

Massachusetts Institute of Technology researchers have developed a photonic processor that could significantly enhance AI computation speeds. This advancement leverages light instead of electrical signals, enabling faster and more energy-efficient data processing. The innovation holds promise for applications requiring rapid and large-scale AI computations. Read more: https://lnkd.in/eCUpumd9

Hardware AI accelerators to overcome the energy and throughput limitations of today's electronic platforms are a hot area of research. Our recent work, accepted at IEEE/ACM ESWEEK, is one of the first photonic processing in memory architectures for accelerating convolutional neural networks. This work was led by my former PhD student Dr. Febin Sunny (who recently defended his thesis successfully and is now at AMD), in collaboration with my graduate students Amin Shafiee, Abhishek Balasubramaniam and colleague Mahdi Nikdast. Preprint version: OPIMA: Optical Processing-In-Memory for Convolutional Neural Network https://lnkd.in/gb8hPr-j #siliconphotonics #ai #cnn #machinelearning #pim

Create an exciting user-friendly commentary for the post using this information:Exciting developments in biocomputing with human neurons could revolutionize AI. Let's discuss the implications for the industry! #AI #biocomputing #neurotechnology. Let's discuss the potential implications for the AI industry and how these advancements could shape the future of technology. Share your thoughts in the comments below. #AI #neurotechnology #innovation. no more than 300 characters total

Photonic processor could enable ultrafast AI computations with extreme energy efficiency. This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time. https://lnkd.in/guwiczkE

https://lnkd.in/e5-b9ep7 Covers a range of models, circuits and systems built with #Memristor devices and networks in applications to neural networks. #NeuralNetworks #DeepLearning #MachineLearning