Hardware Innovations for AI in Local Computing

An exciting week for #neuromorphic computing and decreasing the compute power required for #AI and #ML! For more on this topic, see my previous post: https://lnkd.in/g3EeG3Ku https://lnkd.in/gia2EVK2 Researchers report the creation of the first #roomtemperature, #lowpower (20 pW) moiré #synaptic #transistor. It is #graphene based. "The asymmetric gating in dual-gated moiré heterostructures realizes diverse biorealistic neuromorphic functionalities, such as reconfigurable synaptic responses, spatiotemporal-based tempotrons and Bienenstock–Cooper–Munro input-specific adaptation. In this manner, the moiré synaptic transistor enables efficient compute-in-memory designs and #edgehardware accelerators for #artificialintelligence and #machinelearning. Key points: Design and Material Composition: The synaptic transistor is designed to mirror human brain function, in its ability to process and store information concurrently. This mimics the brain's capability for higher-level cognition. The transistor combines two atomically thin materials – bilayer #graphene and hexagonal boron nitride – arranged in a moiré pattern to achieve its #neuromorphic functionality. This innovative structure enables the device to perform associative #learning and recognize patterns, even with imperfect input. Cognitive Functionality: The device’s ability to perform associative learning and pattern recognition, even with imperfect inputs, represents a step towards replicating higher-level cognitive functions in artificial intelligence systems. This research provides a foundation for the development of more efficient, brain-like AI systems, potentially transforming how information processing and memory storage are approached in silico. Operational Stability and Efficiency: Unlike previous brain-like computing devices that required #cryogenic temperatures to function, this new device operates stably at room temperature. It demonstrates fast operational speeds, low energy consumption, and the ability to retain stored information even when power is removed, making it highly applicable for real-world use. Implications for AI and ML: This highlights a shift away from traditional #transistor-based computing towards more energy-efficient and capable systems for AI and ML tasks. This development addresses the high power consumption issue prevalent in conventional #digitalcomputing systems, where separate processing and storage units create bottlenecks in data-intensive tasks. Original article in Nature: https://lnkd.in/gSvyUyYK

🚀 Exciting News in AI: Apple Aims to Bring AI to iPhones 📱 Apple is making a bold move in the world of artificial intelligence (AI) by focusing on running AI directly on its hardware instead of relying on the cloud. In a recent research paper titled "LLM in a Flash," Apple's researchers propose a solution to the current computational bottleneck of running large language models (LLMs) on devices with limited memory. This signifies Apple's intention to catch up with its Silicon Valley competitors in generative AI, a technology that powers applications like ChatGPT. Here are the key takeaways from Apple's research: 🔹 Apple's research offers a solution to running LLMs on devices with limited memory, paving the way for effective inference on iPhones. 🔹 Apple's focus on AI running directly on iPhones is a departure from its competitors, such as Microsoft and Google, who prioritize delivering AI services from their cloud platforms. 🔹 This move by Apple aligns with the trend of AI-focused smartphones entering the market, with estimations of over 100 million AI-focused smartphones shipping by 2024. 🔹 Bringing AI to smartphones has the potential to revolutionize the user experience, allowing for new innovations and anticipating users' actions. 🔹 Running AI models on personal devices presents technical challenges, but it could result in faster response times, offline capabilities, and enhanced privacy. Apple's research paper offers a glimpse into the company's cutting-edge research and sets a precedent for future AI development. Optimizing LLMs for battery-powered devices is a growing focus for AI researchers, and Apple's approach opens doors for harnessing the full potential of AI in various devices and applications. As the AI landscape continues to evolve, it is crucial for technology companies to ensure that the benefits of AI reach the widest audience possible. Apple's focus on running AI on iPhones presents an opportunity to enhance user experiences and protect privacy. To learn more about Apple's research on AI, read the full article here: https://lnkd.in/gUNFTEE4 #AI #ArtificialIntelligence #Apple #iPhone #Inference #GenerativeAI #Smartphones #Technology #Privacy #UserExperience #FutureOfTech

Imagine holding the power of a sophisticated AI in the palm of your hand—literally. Recent advancements by Microsoft have made it possible to run highly capable AI models directly on phones and laptops without significant performance sacrifices. This innovation could revolutionize how we interact with technology, opening up new and exciting possibilities for AI applications. When ChatGPT first debuted in November 2023, its capabilities were impressive but came with a significant drawback: the need for cloud-based processing due to the model's enormous size. Fast forward to today, and we are witnessing a paradigm shift. Advanced AI models, once requiring substantial computational resources, can now operate efficiently on everyday devices like smartphones and laptops. This breakthrough not only makes AI more accessible but also promises a future where AI can function independently of the cloud, enhancing privacy and responsiveness. This transformation is exemplified by Microsoft's Phi-3-mini, a small yet powerful AI model demonstrating that bigger isn't always better and that the future of AI is secure and responsive. 📱 Compact Power: AI models like Microsoft's Phi-3-mini are small enough to run on smartphones and laptops without compromising performance. 🌐 Cloud Independence: These models open up new use cases by enabling AI to operate without constant cloud connectivity, enhancing privacy and reducing latency. 🎓 Smart Training: Microsoft's approach of training smaller models with carefully curated data shows that quality, not just quantity, of data can lead to superior AI performance. 🚀Innovation in AI: The success of Phi-3-mini highlights that the future of AI development may focus on refining and optimizing smaller models rather than just scaling up. This shift in focus inspires us to imagine AI technology's endless possibilities and potential. 🖥️ Future Applications: Running AI locally on devices can lead to deeply integrated applications, providing users with seamless and intelligent experiences across their tech ecosystem. #AI #MachineLearning #TechInnovation #MobileAI #MicrosoftAI #ChatGPT #Phi3mini #TechTrends #FutureOfAI #OnDeviceAI #SmartTechnology

"An innovative approach to artificial intelligence (AI) enables reconstructing a broad field of data, such as overall ocean temperature, from a small number of field-deployable sensors using low-powered "edge" computing, with broad applications across industry, science and medicine. "We developed a neural network that allows us to represent a large system in a very compact way," said Javier Santos, a Los Alamos National Laboratory researcher who applies computational science to geophysical problems. "That compactness means it requires fewer computing resources compared to state-of-the-art convolutional neural network architectures, making it well-suited to field deployment on drones, sensor arrays and other edge-computing applications that put computation closer to its end use."" #edgecomputing #ai