MIT Quantum Photonics Laboratory’s Post

Revolutionary Neuromorphic Vision Platform Combines Optical Sensing and Neural Network Processing Researchers at Peking University have developed a groundbreaking reconfigurable neuromorphic computing platform that integrates sensing, memory, and computing functions into a single device. This platform combines a 20×20 array of phototransistors with Mott memristors, creating a system capable of performing various tasks, from computer vision to neural network-based algorithms. Inspired by the efficiency of biological vision systems, the platform supports multiple encoding methods (analog, spiking, and spatiotemporal) and is compatible with various neural network architectures, including CNNs, RNNs, and SNNs. By merging optical sensing with data processing and memory functions, the system offers significant advantages, such as lower power consumption, reduced latency, and improved efficiency for real-time image processing applications. This innovation could pave the way for more energy-efficient, scalable, and flexible vision AI systems with potential applications in diverse fields. Further development will focus on improving power efficiency and optimizing the platform for use in natural and low-light conditions. #VisionAI #NeuromorphicComputing #MachineLearning #AIResearch #Memristors #ReconfigurableTech #PekingUniversity #SensingAndComputing #Phototransistors #EnergyEfficientAI #AIHardware #FutureOfAI #DeepLearning #NeuralNetworks #RealTimeProcessing #WissenResearch

Think Moore's Law will keep up with AI? Think again — light-based chips might just outshine silicon. 🔦 ➡️ The rapid growth in AI’s computing demands is outpacing traditional silicon chips, prompting researchers to explore optical computing, which uses photons instead of electrons. ➡️ Light-based systems promise faster processing and greater efficiency, crucial for AI’s future. Recent advancements show optical neural networks (ONNs) performing tasks like matrix multiplication, a core component of AI, more efficiently than electronic systems. ❓ However, challenges remain, including scaling these systems to compete with giants like Nvidia. As optical computing progresses, the potential to revolutionize AI and other high-demand fields grows. Will this breakthrough redefine the future of technology and energy consumption? Read the full story on Quanta Magazine: https://lnkd.in/e4y2fw8q #Chips #AI #Computing #Photonic #Optical ---- 💡 𝗜𝗳 𝘆𝗼𝘂 𝗲𝗻𝗷𝗼𝘆𝗲𝗱 𝘁𝗵𝗶𝘀 𝗰𝗼𝗻𝘁𝗲𝗻𝘁, 𝗯𝗲 𝘀𝘂𝗿𝗲 𝘁𝗼 𝗱𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗺𝘆 𝗻𝗲𝘄 𝗮𝗽𝗽 𝗳𝗼𝗿 𝗮 𝘂𝗻𝗶𝗾𝘂𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝘆𝗼𝘂𝗿 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 - you can have real-time insights, recommendations (a lot more than I share here) and conversations with my digital twin via text, audio or video in 28 languages! Join >5000 users who went before and go to app.thedigitalspeaker.com to sign up and take our connection to the next level! 🚀

Think Moore's Law will keep up with AI? Think again — light-based chips might just outshine silicon. 🔦 ➡️ The rapid growth in AI’s computing demands is outpacing traditional silicon chips, prompting researchers to explore optical computing, which uses photons instead of electrons. ➡️ Light-based systems promise faster processing and greater efficiency, crucial for AI’s future. Recent advancements show optical neural networks (ONNs) performing tasks like matrix multiplication, a core component of AI, more efficiently than electronic systems. ❓ However, challenges remain, including scaling these systems to compete with giants like Nvidia. As optical computing progresses, the potential to revolutionize AI and other high-demand fields grows. Will this breakthrough redefine the future of technology and energy consumption? Read the full story on Quanta Magazine: https://lnkd.in/gpMCdmVD #Chips #AI #Computing #Photonic #Optical ---- 💡 𝗜𝗳 𝘆𝗼𝘂 𝗲𝗻𝗷𝗼𝘆𝗲𝗱 𝘁𝗵𝗶𝘀 𝗰𝗼𝗻𝘁𝗲𝗻𝘁, 𝗯𝗲 𝘀𝘂𝗿𝗲 𝘁𝗼 𝗱𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗺𝘆 𝗻𝗲𝘄 𝗮𝗽𝗽 𝗳𝗼𝗿 𝗮 𝘂𝗻𝗶𝗾𝘂𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝘆𝗼𝘂𝗿 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 - you can have real-time insights, recommendations (a lot more than I share here) and conversations with my digital twin via text, audio or video in 28 languages! Join >5000 users who went before and go to app.thedigitalspeaker.com to sign up and take our connection to the next level! 🚀

Think Moore's Law will keep up with AI? Think again — light-based chips might just outshine silicon. 🔦 ➡️ The rapid growth in AI’s computing demands is outpacing traditional silicon chips, prompting researchers to explore optical computing, which uses photons instead of electrons. ➡️ Light-based systems promise faster processing and greater efficiency, crucial for AI’s future. Recent advancements show optical neural networks (ONNs) performing tasks like matrix multiplication, a core component of AI, more efficiently than electronic systems. ❓ However, challenges remain, including scaling these systems to compete with giants like Nvidia. As optical computing progresses, the potential to revolutionize AI and other high-demand fields grows. Will this breakthrough redefine the future of technology and energy consumption? Read the full story on Quanta Magazine: https://lnkd.in/e4y2fw8q #Chips #AI #Computing #Photonic #Optical ---- 💡 𝗜𝗳 𝘆𝗼𝘂 𝗲𝗻𝗷𝗼𝘆𝗲𝗱 𝘁𝗵𝗶𝘀 𝗰𝗼𝗻𝘁𝗲𝗻𝘁, 𝗯𝗲 𝘀𝘂𝗿𝗲 𝘁𝗼 𝗱𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗺𝘆 𝗻𝗲𝘄 𝗮𝗽𝗽 𝗳𝗼𝗿 𝗮 𝘂𝗻𝗶𝗾𝘂𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝘆𝗼𝘂𝗿 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 - you can have real-time insights, recommendations (a lot more than I share here) and conversations with my digital twin via text, audio or video in 28 languages! Join >5000 users who went before and go to app.thedigitalspeaker.com to sign up and take our connection to the next level! 🚀

Embedded Artificial Intelligence http://bit.ly/44B9GVO provides an overview of the latest research results and activities in industrial embedded AI technologies and applications, based on close cooperation between three large-scale ECSEL JU projects, AI4DI, ANDANTE, and TEMPO. Editors: Ovidiu Vermesan, SINTEF, Norway. Mario Diaz Nava, STMicroelectronics, France. Björn Debaillie, imec, Belgium. #Embedded #artificialintelligence #edgeartificialintelligence #intelligent #embedded #systems edge industrial computing, neuromorphic computing, AI #hardwareplatforms , #Neuromorphic #architecture , neural networks, spiking neural networks, benchmarking, #DeepLearning , machine learning, industrial internet of things, image processing, semiconductor manufacturing, trustworthy microelectronics, AI-enabled software applications.

Researchers from MIT have developed a fully integrated photonic chip that can perform all essential computations of a deep neural network using light, overcoming previous limitations where certain operations required slower, energy-intensive electronic components. Key Features of the Photonic Processor ➡️ 𝐔𝐥𝐭𝐫𝐚𝐟𝐚𝐬𝐭 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧: The processor completes machine-learning tasks in less than half a nanosecond with over 92% accuracy, comparable to traditional hardware but significantly faster. ➡️ 𝐄𝐧𝐞𝐫𝐠𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: By maintaining operations in the optical domain until the final output, the chip reduces energy consumption, making it ideal for real-time applications like lidar and high-speed telecommunications. ➡️ 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: Fabricated using commercial foundry processes similar to those used for CMOS chips, this technology is poised for scalable manufacturing and integration into existing systems. 🔗 https://lnkd.in/dk-YSj2N #Photonics #AI #Innovation #TechRevolution #EnergyEfficiency 🌟

Breakthrough in Optical Neural Networks! Researchers have developed an ultrafast Convolutional Optical Neural Network (ONN) capable of processing images through highly scattering media at the speed of light, merging the power of neural networks with cutting-edge optoelectronics. This convergence at the intersection of domain-specific knowledge is paving the way for groundbreaking applications across various fields. Read article https://lnkd.in/dBp2JUaW Applications & Value: - Real-time medical imaging recognition through turbid tissues could revolutionize diagnostics and surgeries; - Enhances monitoring and object detection for industrial Safety even in environments obscured by smoke or dust, ensuring higher safety standards; - Boosts the capability of robots and drones to navigate and operate in visually challenging environments; to achieve an energy-efficient and high-speed vision tasks. This integration not only enhances the speed and energy efficiency of imaging technologies but also broadens the potential for high-impact applications in everyday technology. The future is already here! 👩🚀 #OpticalNeuralNetworks #AI #MachineVision #TechInnovation #FutureofImaging

"The rapid development of technologies such as the internet, mobile communications, and artificial intelligence has dramatically increased the demand for high-capacity communication systems. Among various solutions, mode-division multiplexing (MDM) has emerged as a crucial technique, utilizing spatial modes like orbital angular momentum (OAM) to enhance communication capacity. In a recent study, a team of scientists led by Professor Jian Wang from Huazhong University of Science and Technology introduced a flexible mode-switching system based on an optical neural network chip. This system is capable of switching between different OAM modes in a multimode fiber, a critical function for modern optical communication networks. The optical neural network chip provides the necessary flexibility, enabling arbitrary mode switching among the three OAM modes within the fiber." #optical #communications #oam

MIT researchers report on a #photonic chip that performs all the key operations of a deep neural network using light, enabling faster and more energy-efficient #AI computations. ✅ The chip integrates optics and electronics to perform nonlinear operations directly on the chip. ✅ Performance of the chip is comparable to traditional hardware, but it can complete computations in less than half a nanosecond. Massachusetts Institute of Technology https://lnkd.in/e5cTuTJ6

What advancements are enabling faster and more energy-efficient AI computations? Researchers at MIT have developed a photonic processor that uses light to perform deep neural network operations on a chip, enabling ultrafast AI computations with extreme energy efficiency. This innovation opens the door to high-speed processors capable of real-time learning, significantly enhancing the performance of AI systems. The use of photonics in computing represents a substantial leap forward in processing capabilities, potentially transforming various applications that rely on rapid data analysis and machine learning. #AI #Photonics #MIT #MachineLearning Read More: 🔗https://buff.ly/3CVyX5c