Electronics in AI Summit: AI Hardware Innovations

🚀 Revolutionizing Efficient LLM Inference with "LLM in a Flash" Deploying large language models (LLMs) on edge and IoT devices is notoriously hard. The biggest bottleneck? Memory. Traditional methods struggle under the constraints of DRAM capacity and hardware limits. That’s where "LLM in a Flash" comes in — a groundbreaking approach that leverages flash memory to optimize LLM inference without requiring massive DRAM. ✨ Key Techniques That Power It: Windowing: Splits the model into smaller chunks, loading only what’s needed into DRAM during inference. Row-Column Bundling: Organizes parameters to minimize flash ↔ DRAM transfers, especially effective for transformers’ attention layers. KV Caching: Integrated with Hugging Face Transformers to reduce redundant computation. 📈 The Trade-off: Slightly higher latency, but massive gains in efficiency, scalability, and feasibility for edge deployment. For many real-world applications, that’s a worthwhile exchange. This innovation opens the door for resource-constrained devices to run LLMs efficiently, making edge AI far more practical. 🔎 Full paper: LLM in a Flash: Efficient Large Language Model Inference https://lnkd.in/dMQi-AGj ) 👉 Have you tried deploying LLMs in constrained environments? What bottlenecks have you hit, and how did you work around them? #LLM #EdgeComputing #AIOptimization #TransformerModels #EfficientInference #SystemDesign

Seoul National University of Science and Technology (SEOULTECH) has developed artificial synapses that mimic human brain function for next-gen AI chips. Dr. Eunho Lee and his team at SEOULTECH have designed organic semiconductors with glycol side chains to improve artificial synapses, enhancing ion transport efficiency. These advancements could lead to more efficient AI hardware, such as ultra-low-power co-processors for wearables and IoT devices, and stable bioelectronic interfaces for medical applications. Dr. Lee said that controlling ion motion in soft semiconductors could reshape AI processing, offering energy-efficient and adaptive hardware solutions. Read more: https://lnkd.in/eHHfmfYD 📰 Subscribe to the Life AI Weekly Newsletter: https://lnkd.in/eC5-u69w #ai #artificialintelligence #ainews #biotech #healthcareai

SiFive Sets Out to Integrate More RISC-V Cores into AI Chip Designs • The Register https://lnkd.in/gA33Vf3F Exciting Developments in RISC-V: SiFive’s Second-Gen Intelligence Cores and AI Solutions SiFive is making waves in the AI space with its newly unveiled second-generation Intelligence cores, revolutionizing how we approach edge AI applications. Here’s the latest scoop that every tech enthusiast should know: Key Highlights: New Intelligence Cores: X160 and X180 targeting low-power applications like IoT and robotics. Core Architecture: Eight-stage dual-issue in-order superscalar processor designed for tensor cores and matrix units. Innovative Interfaces: Introduction of Scalar Coprocessor Interface (SSCI) enhances direct access to CPU registers. Performance Improvements: The X390 Gen 2 boasts 4x compute and 32x data throughput over its predecessor. Enhanced cache hierarchy for improved performance metrics and die area efficiency. Future Readiness: With first customer silicon expected by Q2 2026, SiFive is positioning itself as a leader in AI accelerators. 💡 Curious about how these advancements can impact your projects? Let’s discuss! Share and comment below! Source link https://lnkd.in/gA33Vf3F

5G-Advanced: The Path to 6G! 🌐 A new 5G Americas white paper (3GPP Release 18+) shows how networks are shifting from fast to intelligent, sustainable, and immersive. Key insights below ⤵️ ✨ Layered Intelligence: From boosting signal quality and spectrum efficiency (L1) to smarter mobility management and dynamic resource allocation (L2 & L3), AI is transforming every layer of the network. Use cases like beamforming and lifecycle management are unlocking major efficiencies. 🌐 Cross-Layer Optimisation: AI enables intent-driven networking and holistic lifecycle management, ensuring networks run seamlessly end-to-end. 📡 RAN Innovations: With Open RAN and RAN Intelligent Controllers (RIC), AI is pushing programmability and real-time resource optimisation to new levels. 🤖 Generative AI in Telecom: Beyond chatbots, generative AI is revolutionising telecom with intent prediction, synthetic data generation, OSS/BSS automation, semantic communication, and smarter troubleshooting. 🔒 Responsible AI: The future of AI-driven networks must also be trustworthy, with transparency, explainability, privacy safeguards, and bias mitigation at the core. 💬 “AI offers unprecedented capabilities for enabling automation and enhancing network intelligence. By integrating AI across multiple layers of the network and in the device, we can achieve seamless connectivity and drive the evolution of telecommunications,” said Dr. Eren Balevi, Staff Engineer at Qualcomm Technologies, Inc. #4G #LTE #5G #5GNR #6G #AI #IoT #MobileNetworks #Telecoms

What if every wireless signal around us could do more than just connect us? 🤯 I'm incredibly excited to share a video that explains AirNeuron, a truly groundbreaking project I've been involved with! We're reimagining distributed computing by transforming the ambient wireless signals (yes, your everyday Wi-Fi, 4G, 5G, and Bluetooth!) into a virtual neural network right in the air. Imagine a world where your wireless devices aren't just sending and receiving data, but acting as neurons, with their signals serving as synapses, collectively performing complex computations – just like a brain operating through the air! Here’s why AirNeuron is such a game-changer: Radical Energy Efficiency: We're literally harnessing and recycling 'leftover' electromagnetic signals for power and processing. This means drastically less energy waste, with the potential for up to a 75% reduction in carbon footprint compared to traditional cloud data centers! Advanced Virtual Neural Cells (VNCs): Our VNCs are at the cutting edge, featuring quantum-enhanced processing for ultra-secure communications, bio-inspired mechanisms for dynamic resource allocation, and even self-destructive security protocols for sensitive data protection. Decentralized & Super Resilient: Built on a dynamic mesh network topology, AirNeuron ensures unmatched fault tolerance and scalability, freeing us from the limitations of centralized computing models. Real-time AI & IoT at the Edge: This system enables powerful edge computing and federated learning directly where the data is generated. Think dramatically reduced latency for AI inference and vastly enhanced capabilities for IoT devices. The potential applications are immense – from making our smart homes and public spaces truly intelligent through ambient awareness, to transforming industrial automation and enabling next-gen collaborative robotics. Dive deeper into how we're making computing more sustainable, available, and intelligent by giving wireless signals a powerful "second job"! Watch the video to see AirNeuron in action and understand the mechanics behind this innovative approach. Let me know what you think! 👇 #AirNeuron #DistributedComputing #WirelessTechnology #AI #IoT #SustainableTech #EdgeComputing #SmartCities #Innovation #NeuralNetworks #FutureOfTech

Quantum Computing & AGI: Catalysts for Transformation in Energy and Healthcare The pace of innovation in emerging technologies is accelerating, and two domains stand out for their transformative potential: Quantum Computing and Artificial General Intelligence (AGI). In the energy sector, quantum computing is redefining how we approach complex simulations—from optimizing grid performance and fuel efficiency to predictive maintenance and environmental modeling. These capabilities are not just theoretical—they’re paving the way for smarter, more sustainable operations. In healthcare, AGI introduces a paradigm shift. Beyond traditional AI, AGI systems can learn, reason, and adapt across diverse tasks. This opens doors to intelligent diagnostics, personalized treatment planning, and real-time decision support—ultimately enhancing patient care and operational agility. As these technologies mature, their convergence with enterprise platforms, IoT, and data analytics will unlock new possibilities. The challenge lies not just in adoption, but in aligning them with real-world needs, ethical frameworks, and scalable architectures. The future is not just digital—it’s intelligent, adaptive, and quantum-powered. #QuantumComputing #AGI #DigitalTransformation #EnergyInnovation #HealthcareTech #AI #EmergingTechnologies #SmartSystems #TechLeadership

AI meets Embedded Systems – powering the next generation of intelligent devices. From healthcare wearables that detect anomalies in real time to manufacturing systems that predict failures before they happen, AI-embedded systems are shaping the future across industries. Our latest blog explores: 🔹 Real-world applications in healthcare, automotive, manufacturing & more 🔹 Key technologies enabling AI at the edge (TinyML, NPUs, lightweight runtimes) 🔹 Challenges like latency, data privacy & resource limits—and how to overcome them Read the full article here: https://lnkd.in/g2YqR5_H #AI #EmbeddedSystems #EdgeAI #Innovation #Technology #TravancoreAnalytics

🚀 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗯𝗲𝘆𝗼𝗻𝗱 𝗰𝗼𝗱𝗲 𝗮𝗻𝗱 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗳𝗮𝗯𝗿𝗶𝗰 𝗼𝗳 𝗿𝗲𝗮𝗹𝗶𝘁𝘆. Two 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝘀 caught my eye this week: 🔹 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (QML) for Semiconductors Researchers have used a QML model called Quantum Kernel-Aligned Regressor (QKAR) to design semiconductors up to 20% more efficiently than classical ML. Imagine ML models not just running on chips—but actually helping to create the next generation of them. 🤯 👉 https://lnkd.in/dsaqpPvs? 🔹 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 in AI We’re moving past purely digital learning toward AI that adapts to the physical world. Think drones navigating unpredictable environments or robotics infused with physics-informed ML. This shift from “data intelligence” to “physical intelligence” could redefine how machines coexist with us. 👉 https://lnkd.in/dZKeCmBR? As an ML engineer, I find both directions exciting: QML could optimize edge devices, IoT, and chip pipelines. Physical intelligence could unlock safer, more reliable AI in robotics and real-world systems. 💡 I’d love to know: Where do you see the bigger impact—quantum ML shaping chips, or physical intelligence shaping robotics? #MachineLearning #QuantumML #PhysicalIntelligence #Innovation #MLinProduction

🚀 Introducing the Arducam UVC AI Camera Module (IMX500) We’re thrilled to unveil a next-generation AI camera module that redefines what's possible at the edge. At HiTechChain Sweden AB, we believe in blending hardware performance with intelligent processing—and this module does just that. 🔍 Key Highlights IMX500 sensor with onboard AI compute — vision + inference in one chip UVC Compatible — plug & play over USB, broad compatibility Edge AI processing — works even without cloud connection; low latency, enhanced privacy Universal AI Camera Series — extends beyond Sony’s AITRIOS™ templates via Arducam’s AI Model Zoo 🌍 Why It Matters Instant Insights – Real-time vision AI in applications like security, robotics, industrial automation. Privacy & Reliability – Data processed on-device means sensitive content never has to travel far. Versatile – Works in varying connectivity conditions; great for remote locations or constrained networks. 👉 Check it out here: HiTechChain — Arducam UVC AI Camera Module, powered by IMX500 #EdgeAI #ComputerVision #Arducam #IMX500 #IoT #AI #HiTechChain

🎤 Live from EUSIPCO 2025 in Palermo! 🇮🇹 📢 We’re thrilled to share that Stefano Ciapponi is presenting our latest research on "Exploiting Neural Audio Codecs for Edge-to-Gateway Speech Processing" at the European Signal Processing Conference. Our work explores how Neural Audio Codecs (NACs)—inspired by SoundStream—can be leveraged not just for compression, but as powerful tools for signal reconstruction and feature extraction in edge computing environments. 🔍 Highlights from our study: 40× audio compression with only a 3% increase in WER for transcription tasks and 94.6% accuracy on end-to-end intent classification. We achieve real-time performance on ARM Cortex-A53 with 12–8× less energy consumption than baseline models. This opens up exciting possibilities for IoT applications, enabling efficient and intelligent speech processing directly on resource-constrained devices. 👏 Huge thanks to the EUSIPCO community for the opportunity to share our work—and congrats to Stefano for representing us so well! #EUSIPCO2025 #EdgeAI #NeuralAudioCodecs #SpeechProcessing #IoT #SignalProcessing #AIResearch #EnergyEfficiency #SoundStream #EmbeddedAI