My journey into Edge AI at eInfochips

Let’s be honest: taking a non-trivial AI and/or computer vision solution from a development environment to a low-power embedded device at scale remains a steep climb in 2025. It’s where elegant algorithms and machine learning models meet the harsh realities of unconstrained environments, complex firmware integration, unforgiving hardware limitations, and scalability challenges. - The Lab-to-Reality Gap: AI models that perform well in the lab experience an unacceptable accuracy drop when downsized and quantized, struggle in messy, unpredictable conditions of real-world lighting and environments, or fail on data from lower-end sensors. - A Fragmented Ecosystem: running inference on embedded devices remains a jungle of proprietary toolchains and SDKs for different AI accelerators. Expertise in one stack doesn't directly translate to another. Productivity and automation tools are often vendor-confined or do not support more complex cases. - Hardware Constraints: The trade-offs between off-the-shelf hardware limitations and the cost of custom development—all while battling device costs, power budgets, performance limitations, and the sheer challenge of fitting complex neural networks into constrained embedded AI accelerators. - Embedded Plumbing: The tedious and complex work of hardware bring-up, wrestling with Board Support Packages (BSPs), building secure boot, Over-the-Air (OTA) update mechanisms, and taming the low-level drivers are essential for the systems to function, but they are a world away from your AI core innovation. - Regulatory Hurdles: Products with digital elements must navigate a growing maze of compliance demands, e.g., the EU's AI and Cyber Resilience Acts, which mandate verifiable security-by-design and robust governance from the ground up. - Scaling and Deployment: The operational challenge of moving from a working prototype to reliably provisioning, managing, and updating a fleet of hundreds, thousands, or more devices in the field, each with its own potential for hardware variance. Before these challenges lead to budget overruns, roadmap delays, and a widening gap between vision and reality, seeking specialized assistance may be a strategic decision to de-risk your project and accelerate your time-to-market. Are you ready to talk to an experienced partner who can help you navigate these complexities and accelerate your product's journey to market? Let's connect! #EmbeddedAI #ComputerVision #EdgeAI #EmbeddedSystems #AIStrategy #Innovation #Engineering #IoT #Estigiti

Setting aside the various speculations about AI, I would like to share a realistic forecast. In the next 3 to 5 years, new-generation AI models will begin to run on powerful yet energy-efficient GPUs that are capable of operating on mobile devices. This advancement will lower costs while prioritizing privacy. The usage model will likely continue to be based on monthly subscriptions. AI will evolve into a more sophisticated personal assistant, becoming significantly more intelligent and capable. However, the emergence of Artificial General Intelligence (AGI) is expected to take over 10 years. During this time, investments in hardware will remain crucial. Specifically, there will be a significant increase in interest and investment in ARM-based processors. In the coming years, many of today’s AI models will be replaced by more optimized versions that can deliver improved performance on CPUs.

As we dive into 2025, the AI hardware landscape is undergoing significant transformations. One of the most notable trends is the development of custom AI chips, such as Neural Processing Units (NPUs) and Tensor Processing Units (TPUs), which are designed to handle specific tasks like training AI models and real-time inference. These custom silicon chips offer faster processing, lower latency, and reduced energy consumption, making them a strategic asset for enterprises and cloud providers focused on sustainable development. Companies like Nvidia and AMD are leading the charge in this area, with Nvidia's latest advancements and AMD's price-to-performance ratio being particularly noteworthy. Another area of growth is edge AI, which allows devices to process AI tasks without an internet connection. This is particularly useful in industries where security and latency are a concern, such as in autonomous vehicles and smart sensors. Edge AI is faster and more secure than cloud-based AI, making it an attractive option for companies looking to deploy AI solutions. The rise of AI has also led to an increase in demand for data centers, with global capacity expected to double by 2027. To meet this demand, companies are investing in sustainable data center designs, including liquid cooling, advanced power management systems, and AI-optimized rack designs. As AI continues to evolve, we can expect to see even more innovative developments in the field of AI hardware. From multimodal AI systems to agentic AI, the possibilities are endless. If you're looking to stay ahead of the curve and leverage the power of AI for your business, consider consulting with an AI expert. #AI #ArtificialIntelligence #MachineLearning #HardwareTrends #Sustainability #EdgeAI #DataCenters #Innovation #Technology 🔄 Share 👍 React 🌐 Visit www.aravind-r.com #AravindRaghunathan

𝗛𝗼𝘄 𝗟𝗟𝗠𝘀 & 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗔𝗿𝗲 𝗣𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗖𝘂𝘀𝘁𝗼𝗺 𝗔𝗜 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿𝘀—𝗕𝗲𝘆𝗼𝗻𝗱 𝗖𝗨𝗗𝗔 By 2025, visionaries aren’t just asking which #GPU wins—they’re asking which software stack enables breakthroughs. Welcome to the era where #LLMs unlock accelerators. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀  • #Huawei just open-sourced #CANN, its equivalent to #CUDA for Ascend AI GPUs. This move, steeped in geopolitics and the scramble for sovereignty, is aimed at breaking the two-decade dominance of CUDA and sparking ecosystem innovation.  • #Intel’s #OpenVINO 2025.2 now supports a rich set of LLMs (#Qwen3, #Phi‑4, #Mistral‑7B, #SD‑XL), runs smarter on built-in #NPUs with KV cache compression and #LoRA adapters, and optimizes inference across CPUs and GPUs. 𝙒𝙝𝙮 𝙙𝙤𝙚𝙨 𝙩𝙝𝙞𝙨 𝙚𝙭𝙘𝙞𝙩𝙚 𝙢𝙚 𝙖𝙨 𝙖 𝙥𝙧𝙤𝙙𝙪𝙘𝙩 𝙨𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙨𝙩? Because whether it’s for hyperscale or edge—driverless cars, wearables, or smart factories—the software layer is the superpower that unleashes hardware potential. 𝗧𝗵𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗳𝗼𝗿 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗟𝗲𝗮𝗱𝗲𝗿𝘀  1. Model → Runtime → Hardware (LLM → Software stack → NPU/TPU): Product success hinges on seamless alignment across all three.  2. Avoiding lock-in: CANN’s open move could enable a new generation of cross-platform AI design—if tools and documentation catch up.  3. Optimizing at the edge: OpenVINO’s NPU optimizations reshape the performance/usability calculus for on-device GenAI—essential for instantaneous inference and user trust. 𝗠𝘆 𝗧𝗮𝗸𝗲 Enterprise AI will continue to demand scale. But edge AI will only win with performance and portability—and that’s powered by software. The future belongs to those who design hardware and the frameworks that unlock it—especially when AI must run inside a car, a wearable, or a factory sensor under real-world constraints. If product teams can work across silicon, software, and strategy—what kind of hardware stacks will that unlock? Do you see a startup ecosystem growing around CANN? Or are frameworks like OpenVINO defining the standards that will shape edge AI’s future? The big question is: 👉 Will we see an open, cross-platform “lingua franca” for AI hardware emerge, or will ecosystems remain siloed under proprietary stacks? Comment below 👇 Let’s discuss. #AI #EdgeAI #EnterpriseAI #ProductLeadership #OnDeviceAI #MLInfrastructure #SoftwareEcosystems

🚀 What happens when AI data centres run out of space? NVIDIA thinks it has the answer. As AI models grow in size and complexity, the demand for computational power is pushing traditional data centres to their limits. Building ever-larger facilities is costly and often unsustainable. 💡 Enter NVIDIA Spectrum-XGS Ethernet — a new networking technology designed to connect AI data centres across vast distances, effectively transforming them into “giga-scale AI super-factories.” 🔑 Key innovations include: • Distance-adaptive algorithms for efficient long-range networking • Advanced congestion control to prevent bottlenecks • Precision latency management for predictable performance • End-to-end telemetry for real-time optimisation 🌍 CoreWeave will be among the first to deploy this technology, unifying multiple data centres into a single AI supercomputer. Why this matters: Instead of overloading local power grids or building massive single-site facilities, companies could distribute infrastructure across locations while still achieving near-seamless performance. Jensen Huang calls this the next stage of the AI industrial revolution — but the real test will be in how well it performs under real-world conditions. ⚡ If successful, this could reshape how the AI industry scales infrastructure and delivers services. 👉 What do you think: Will “scale-across” become the new standard for AI data centres? #AI #DataCenters #NVIDIA #Networking #DigitalTransformation #CloudComputing

The Future of AI Hardware: Efficiency, Specialization, and Sustainability The era of brute-force AI is over. We are at a tipping point in the AI hardware race. The old model was simple: bigger models need bigger GPUs. But this approach is hitting a wall, and not just in performance. It's a wall of cost and unsustainability. Data centers are projected to consume up to 12% of total US electricity within the next three years, a dramatic increase driven largely by massive, general-purpose models.That's a huge strain on the grid and a major business risk. A single query to a generative AI model can consume 10 times more electricity than a standard Google search. But a silent revolution is underway: 1️⃣ Specialization over Brute Force: The shift is from massive, one-size-fits-all GPUs to specialized silicon, like Google's TPUs or custom ASICs designed for specific workloads. These chips can be tens or even thousands of times more efficient than general-purpose CPUs for AI tasks. This "right-tool-for-the-job" approach boosts efficiency and cuts operational costs. 2️⃣ Edge AI: The most innovative work is happening on the edge. By moving AI processing from the cloud to the device itself—on a phone, a factory robot, or a car—we get instant, low-latency performance while drastically reducing the need for continuous, energy-hungry data transfers to the cloud. This on-device processing can reduce energy consumption by 100 to 1,000 times per task compared to cloud-based AI. 3️⃣ The Rise of On-Device Intelligence: For many applications, a large, trillion-parameter model is simply overkill. Small language models (SLMs) and on-device AI are not just a trend; they're a necessity. They offer privacy, speed, and a fraction of the energy footprint. By 2026, some projections suggest that over 40% of PC shipments will be "AI PCs" capable of running these models locally. The future of AI hardware isn't about more power; it's about more intelligence. It's about designing chips that are efficient, specialized, and sustainable by default. How is your organization rethinking its AI hardware strategy to balance performance with sustainability? #AI #Hardware #CTO #Sustainability #EdgeComputing

What if AI could create images literally at the speed of light and use 400x less energy? That’s not sci-fi. Researchers at UCLA just proved it’s possible. Instead of burning through massive amounts of electricity with GPUs, their system uses light itself to do the heavy lifting. The result: AI-generated images in real time, with a fraction of today’s energy cost. Here’s how it works (in simple terms): - A tiny computer makes random light patterns (the “seed”). - A laser carries that pattern through optical modulators. - Outcomes an image with no thousands of digital calculations required. Why this matters: Sustainability - AI data centers already consume as much power as a mid-sized country. Scaling like this is unsustainable. Efficiency - Imagine AR glasses or smartphones generating visuals instantly, without draining your battery. Security - The optical process naturally embeds a “key-lock” system, making data harder to intercept. It’s still early - today’s results don’t yet match Midjourney or Ideogram. But the promise is game-changing: AI that’s not just smarter, but greener. This research makes me wonder: Should the next frontier of AI innovation be less about bigger models and more about better physics? For anyone who wants to dive deeper, I added the link to the full Nature Magazine article covering this breakthrough in the first comment ⬇️ 👉 What’s your take? Could optical AI transform the way we build and deploy generative systems? #ArtificialIntelligence #GenerativeAI #Sustainability #Innovation #FutureOfTech

In scenarios where milliseconds genuinely matter, the traditional reliance on cloud-centric AI can introduce critical delays. As professionals, we often encounter applications where immediate action, not just insight, is paramount for safety, efficiency, or competitive advantage. This is precisely where the synergy of Edge Computing and Edge AI becomes transformative. Edge computing brings data processing and storage closer to the data source itself, whether it's a sensor, a manufacturing robot, or a smart city camera. This fundamentally reduces the distance data needs to travel, cutting down on latency. When we combine this with Edge AI, we are essentially deploying trained machine learning models directly onto these edge devices. This allows for real-time analysis and instantaneous decision-making right where the data is generated, without the need to send everything back to a central cloud server. Imagine a manufacturing line detecting a defect and correcting it instantly, or an autonomous vehicle responding to an obstacle without a network delay. For organizations navigating complex operational environments, embracing Edge AI means drastically reduced latency, enhanced data privacy and security by processing sensitive information locally, and improved resilience in areas with intermittent connectivity. To leverage this, begin by identifying your most time-critical applications and exploring pilot projects where low-latency analytics are non-negotiable. Focus on edge device selection and robust data governance strategies from the outset. What mission-critical applications in your industry do you believe are most ripe for real-time AI analytics at the edge? #EdgeComputing #EdgeAI #ArtificialIntelligence #RealTimeAnalytics #TechTrends

Don't let the AI trip the breaker!! The AI Revolution is so massive that electricity prices are moving in a literal straight line higher. Energy is the new limiting factor to AI growth. Every time a large AI model is trained, tens of thousands of GPUs fire up and then pause in a rapid, repeating cycle. This creates volatile power surges on a scale we've never seen before. The concern isn't just about high energy bills; it's about the stability of the electrical grid itself. A new research paper from Microsoft, OpenAI, and NVIDIA, "Power Stabilization for AI Training Datacenters," directly confronts this challenge. They're not just flagging the problem—they're building the solutions. The paper outlines a multi-layered strategy to "flatten the curve" of energy use, involving smarter software scheduling, more efficient chip designs, and new data center power systems. This is a critical step to ensure the AI revolution doesn't get short-circuited by its own power demands. Read the full paper here: https://lnkd.in/gWnB9K7c #AI #Energy #Tech #Innovation #DataCenters #Sustainability #Economics

💻 AI in Your Laptop? It's Not the Future — It's Already Here I’ve been closely following the recent developments in AI hardware, and one thing’s clear: we’re entering a new era where our everyday devices are getting a lot smarter. We're now seeing the rise of what's being called AI PCs — laptops that come with built-in Neural Processing Units (NPUs) to handle AI tasks directly on the device. No cloud needed. This means things like real-time transcription, smarter photo editing, offline chat assistants, and even personalized system performance — all happening locally. What really stands out to me is how this changes the game: ✅You get better privacy (no data constantly being sent to the cloud), ✅Faster performance (no latency), ✅And surprisingly, longer battery life (since the AI learns how you use your device and optimizes around it). We're seeing this in Microsoft’s new Copilot+ PCs and the latest chips from Qualcomm, Intel, and AMD. It’s not just a hardware update — it’s a shift in how we interact with technology. As someone working in IT, this has got me thinking: How will software development adapt to this? What kind of user experiences can we now create? And how do we support teams as AI becomes a built-in part of their daily workflows? We’re just getting started with this transition, but it’s a space worth watching — or better yet, building in.