How Embedded AI Chips Work: A Deep Dive

This week, we had hundreds of engineers join us live to learn all about implementing ML in their designs. Next up, we’ll show you how to take existing AI models and make them perform at their best for your application. Whether you are working with vision, audio, or other sensor-based tasks, you’ll see how retraining, transfer learning, and synthetic data generation can unlock higher accuracy without starting from scratch. You’ll learn: • Why off-the-shelf models often fail in real-world embedded use cases • How to collect and prepare application-specific datasets • Using synthetic data to close coverage gaps and boost robustness • Deploying optimised, quantised models to Alif’s Ensemble and Balletto MCUs • Practical steps to evaluate and refine accuracy before deployment This is a hands-on, engineer-focused guide using proven workflows on the Edge Impulse (a Qualcomm company) platform with Alif Semiconductor’s fusion processors. 👉 Sign up here to secure your spot: https://lnkd.in/eikbb_kE #EmbeddedAI #EdgeAI #MachineLearning #AIoT #IoT #MCU #ModelTraining #SyntheticData #TransferLearning #Engineers

𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗘𝗱𝗴𝗲 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝗸𝗶𝗻𝗴 Imagine self-driving cars making instant decisions on the road. Or a factory machine detecting a fault and fixing it before it stops production. This is the power of 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗲𝗱𝗴𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴. Instead of sending data to distant servers and waiting for results, AI works directly 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗲𝗱, at the “edge.” That means decisions are made 𝗳𝗮𝘀𝘁𝗲𝗿, with 𝗹𝗲𝘀𝘀 𝗶𝗻𝘁𝗲𝗿𝗻𝗲𝘁 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆, and often with 𝗯𝗲𝘁𝘁𝗲𝗿 𝗽𝗿𝗶𝘃𝗮𝗰𝘆. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝟭: Healthcare devices can instantly detect abnormal heart rates and alert doctors. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝟮: Retail stores can use AI cameras to track stock levels and trigger restocking in seconds. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 In a world where milliseconds can save lives, AI at the edge changes the game for industries like healthcare, manufacturing, retail, and transportation. It’s not just speed. It’s smart, fast, and efficient decision-making. Exactly where it’s needed. #AI #EdgeComputing #RealTimeAI #DigitalTransformation #Industry40 #ArtificialIntelligence #SmartTechnology #IoT #FutureOfWork

AI gets all the headlines. But without embedded computing, it’s just a brilliant idea with nowhere to run. In the race to build smarter cities, safer factories, and more responsive healthcare systems, AI is the brain—but embedded computing is the nervous system. It’s what puts intelligence into motion, at the edge, in real time. Think of it like this: AI says, “I know what to do.” Embedded computing says, “I’ll do it—right here, right now.” From sensors that detect anomalies in milliseconds to edge devices that make split-second decisions without cloud latency, embedded systems are the quiet enablers of intelligent action. Whether it’s a rugged gateway in a remote oil field or a tiny module inside a wearable device, embedded computing brings AI to life where it matters most: close to the data, close to the problem, close to the people. As someone who’s spent years in cloud and now dives deep into embedded, I’m seeing firsthand how this convergence is reshaping industries. It’s not just about performance—it’s about empathy. Solving real-world problems with real-time intelligence. Let’s give embedded its moment. Because AI can’t work without it. #EmbeddedComputing #AI #EdgeIntelligence #IoT #TechWithHeart #Advantech #EmpathyInTech

The MEMS market is set to reach ~$22 B by 2030, driven by IoT, wearables, automotive, and industrial automation growth (~4.6% CAGR). At HCLTech, we see an opportunity to combine Intel Core Ultra AI PCs with MEMS sensor data—bringing AI inference to the edge. Imagine factory vibration sensors feeding into AI PCs running local anomaly detection; or field inspectors using MEMS-enabled tools and on‑device NLP to auto-generate maintenance logs. LSTM for time-series alerts, TinyYOLO for visual QA, MiniLM for summarization, Whisper for voice notes. HCLTech’s Digital Workplace, AI/Analytics, and Engineering Services can deploy this stack—delivering actionable insights, faster decision-making, and cost-efficient and affordable AI. With customers in semiconductor fabs, hi‑tech manufacturing, and MES-driven sites already in our portfolio, we're poised to lead MEMS‑to‑AI digital transformation. Want to learn how this can apply to your plant or sensor‑based business? Let's connect. #MEMS #EdgeAI #AIPC #DigitalWorkplace #SmartManufacturing #IoT #IntelCoreUltra #HCLTech #AffordableAI #AIWorkplaceReady

Spintronics: The Future of Ultra-Low-Power AI Chips What if the heat your chip throws off wasn’t wasted, but turned back into usable energy? That’s exactly what Ultra-Low-Power AI Chips are doing, thanks to spintronics. ♻️ Here’s the magic ♻️ Instead of losing energy when electrons lose their spin orientation, spintronic chips capture that loss and recycle it. The result? ↳ Up to 3× higher efficiency compared to today’s AI hardware. Why it matters: ➡️ Longer battery life Wearables, IoT, and edge devices that last dramatically longer without bigger batteries. ➡️ Cooler, cheaper data centers Less wasted energy means lower cooling costs—and serious savings on power bills. ➡️ More reliable systems Cooler chips = longer lifespans for automotive, industrial, and biomedical electronics. ➡️ Scalable + sustainable Spintronics can be integrated with existing manufacturing processes, making adoption realistic and greener. Who’s driving it: ✳️ KIST, DGIST & Yonsei University, leading breakthroughs in spin-energy recovery ✳️ Global chipmakers, exploring spintronics for next-gen AI accelerators & memory ✳️ European labs, building spin-wave networks for future neuromorphic systems Where it’s headed: 🔹 Data centers slashing megawatts of energy waste 🔹 Smart cities powered by self-sustaining sensors 🔹 Automakers embedding AI without today’s heat & reliability tradeoffs The big shift? Spintronics is turning waste into power. And with it, Ultra-Low-Power AI Chips could redefine how we build everything from smartphones to supercomputers. 💭 How soon do you think we'll see ‘self-powering’ chips become the standard in consumer devices? #Spintronics #Semiconductors #AIChips #SupplyChain #Sourcing

AI at the Edge: From the Cloud to Battery-less Endpoints ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In AI, “the edge” isn’t just one place. It’s a spectrum. Each step away from the cloud changes the constraints, opportunities, and even the definition of intelligence. 1️⃣ Cloud AI Massive compute, infinite storage, and access to vast datasets. Ideal for training foundation models and running complex analytics. But limited by latency, bandwidth, and privacy concerns. 2️⃣ Near-Edge / Edge Servers Located in data centers close to the user or inside enterprise campuses. Lower latency, local data processing, and often the first step toward autonomy in industrial, retail, or smart city applications. 3️⃣ On-Device AI Inside phones, robots, vehicles, cameras, and gateways. Models run where the data is generated, enabling real-time responses, lower bandwidth use, and greater privacy. Advances in AI accelerators are making sophisticated models possible in palm-sized hardware. 4️⃣ TinyML & Ultra-Low-Power AI Running inference on microcontrollers with milliwatts of power. Perfect for IoT sensors, wearables, and embedded devices. Increasingly capable of on-device learning, not just inference. 5️⃣ Battery-less Endpoints The frontier. Devices powered by energy harvesting (solar, RF, vibration) running minimalistic AI locally. No batteries to replace, zero maintenance, and truly distributed intelligence for sensing, monitoring, and actuation. What are the trends shaping this spectrum? * Model efficiency: Quantization, pruning, and architecture search to make AI smaller and faster. * Federated & on-device learning: Models that adapt to users, environments, and contexts without sending raw data back. * Energy-aware AI: Algorithms optimized for power budgets down to microwatts. * Hybrid topologies: Split inference/training between cloud and edge for the best of both worlds. As AI spreads across this topology, the question isn’t just how smart the device is - but where the intelligence lives. To keep it simple, the answer is: “closer to the action.” #EdgeAI #AITrends #TinyML #IoT #CloudComputing #AIHardware #OnDeviceAI #SmartDevices

𝗧𝗶𝗻𝘆𝗠𝗟? Game-changer for deploying machine learning models on tiny, low-power devices like microcontrollers. We're talking about running AI on hardware with just a few kilobytes of memory and a milliwatt of power. This is the intelligence behind your "wake word" device, a smart thermostat, or a sensor that detects a machine's early wear and tear—all without needing a constant cloud connection. So, how does this magic happen? 𝗧𝗵𝗲 𝗧𝗶𝗻𝘆𝗠𝗟 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 The core of TinyML lies in making a neural network extremely small and efficient. This is where 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄 𝗟𝗶𝘁𝗲 (𝗧𝗙𝗟𝗶𝘁𝗲) and its a special version for microcontrollers, 𝗧𝗙𝗟𝗶𝘁𝗲 𝗠𝗶𝗰𝗿𝗼 come in as a 𝗟𝗶𝗴𝗵𝘁-𝗪𝗲𝗶𝗴𝗵𝘁 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗟𝗪𝗡𝗡) frameworks. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴: A model is first trained using a standard framework like TensorFlow on a powerful computer, using large datasets. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: The trained model is then optimized. The key technique here is quantization, which converts the model's floating-point numbers (e.g., 32-bit) into smaller, fixed-point integers (e.g., 8-bit). This drastically reduces the model's size and computational requirements. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: The optimized model is converted into a TFLite flat buffer file (.tflite), which is then deployed to the microcontroller. A specialized TFLite interpreter runs the model directly on the device, performing inferences in real time. This on-device processing offers huge advantages: ✨ 𝗟𝗼𝘄 𝗟𝗮𝘁𝗲𝗻𝗰𝘆: Decisions are made instantly, as data doesn't need to be sent to and from the cloud. 🔒 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Sensitive data remains on the device, reducing the risk of a privacy breach. 🔋 𝗘𝗻𝗲𝗿𝗴𝘆 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: The reduced computations mean devices can run on a tiny battery for months or years. TinyML, powered by frameworks like TFLite, is a huge step toward a world where AI is not just in the cloud, but embedded everywhere, creating smarter, more efficient, and private devices. #TinyML #TensorFlowLite #EdgeAI #MachineLearning #IoT #TechInnovation #MicrocontrollersWrite

The Incredible Shrinking AI: A Game-Changer for IoT Researchers have developed an AI-powered camera that's roughly the size of a coarse grain of salt. This isn't just a novelty; it's a monumental leap for Edge AI. This tiny camera uses a neural network to process images directly on the device, without needing to send data to the cloud. For us as embedded engineers, the implications are massive: 🔹 Enhanced Privacy: Sensitive data (like in medical devices) can be processed locally, drastically reducing security risks. 🔹 Extreme Power Efficiency: It operates on minuscule power, opening doors for long-lasting smart sensors and autonomous devices. 🔹 New Possibilities: Imagine smart dust that can monitor crop health or microscopic robots for non-invasive surgery. This is the very essence of Edge AI – bringing intelligence to the source. The demand for engineers who can build, optimize, and deploy these tiny, powerful systems is only going to grow. The future is not just smart; it's invisibly intelligent. What applications for this technology excite you the most? #EdgeAI #IoT #EmbeddedSystems #Tech #Innovation #FutureOfTech #AI #Engineering #ECE #LinkedInForEngineers

🔍 𝐓𝐞𝐥𝐢𝐭 𝐂𝐢𝐧𝐭𝐞𝐫𝐢𝐨𝐧 𝐒𝐮𝐩𝐞𝐫𝐜𝐡𝐚𝐫𝐠𝐞𝐬 𝐀𝐈 𝐕𝐢𝐬𝐮𝐚𝐥 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐍𝐕𝐈𝐃𝐈𝐀 𝐓𝐀𝐎 𝟔.𝟎 Telit Cinterion’s 𝐝𝐞𝐯𝐢𝐜𝐞𝐖𝐈𝐒𝐄 𝐀𝐈 𝐕𝐢𝐬𝐮𝐚𝐥 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧 now integrates with NVIDIA 𝐓𝐀𝐎 𝟔.𝟎, bringing 𝐥𝐨𝐰-𝐜𝐨𝐝𝐞 𝐀𝐈 to industrial quality control. Manufacturers can now: ✅ Auto-label defects with simple text prompts ✅ Use advanced object pose estimation ✅ Deploy custom AI models faster — no coding required From automotive to healthcare, this integration means 𝐟𝐞𝐰𝐞𝐫 𝐝𝐞𝐟𝐞𝐜𝐭𝐬, 𝐥𝐞𝐬𝐬 𝐝𝐨𝐰𝐧𝐭𝐢𝐦𝐞, 𝐚𝐧𝐝 𝐡𝐢𝐠𝐡𝐞𝐫 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 — all while keeping data secure on-premises. 👉 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐬𝐭𝐨𝐫𝐲 : https://lnkd.in/dGUjG5cV #AI #Industry40 #Manufacturing #VisualInspection #NVIDIA #IoT #Automation #EdgeAI #AIQualityControl #TelitCinterion #SmartManufacturing #DigitalTransformation

The Future of AI is Moving to the Edge 🚀 Study Sample Pages: https://lnkd.in/dMt3zU97 According to Regal Intelligence, the global Edge AI market is set to grow from $22.48 billion in 2025 to $106.8 billion by 2033, at an impressive CAGR of 18.9%. What’s driving this surge? - Real-time data processing - Reduced reliance on cloud infrastructure - Advances in dedicated AI chips - Expanding use in automotive, robotics, and IoT But challenges remain: - Complex network implementation - Lack of unified industry standards Want to explore key players, growth opportunities, and regional trends? Read the full report here 👉 https://lnkd.in/d7kchQzg Leading Players of Edge AI include: Qualcomm Huawei Samsung Electronics Apple MediaTek Intel Corporation NVIDIA IBM Micron Technology AMD Meta Tesla Google Microsoft Imagination Technologies Cambricon (China) Tenstorrent Blaize General Vision (US) Mythic Zero ASIC Applied Brain Research Horizon Robotics Ceva, Inc. Graphcore SambaNova Hailo Axelera AI #EdgeAI #ArtificialIntelligence #AIChips #IoT #TechTrends #MarketResearch #RegalIntelligence