Power efficiency has become the new currency in AI, and no company exemplifies this shift better than EMAS. Founded by Professor Mohamed Ali as a spinoff from his groundbreaking research at NTU Singapore, this innovative startup is revolutionizing edge AI with semiconductor technology that delivers unprecedented power efficiency for battery-constrained devices.

The story begins in 2018 when Ali and his team set out to examine the entire computing stack from applications down to nanotechnology devices. Their research led to a remarkable breakthrough: a chip architecture that brings memory and compute components closer together, resulting in power efficiency 10-100 times better than competing solutions. Unlike other processors that claim low power consumption only during standby, EMAS's chip maintains ultra-low power usage while actively processing data—the true measure of efficiency for AI applications.

Mark Gornson, CEO of EMAS's Semiconductor Division, brings 46 years of industry experience to the team, having worked with giants like Intel and ON Semiconductor. After seeing the benchmarks of EMAS's technology, he came out of retirement to help commercialize what he recognized as a game-changing innovation perfectly timed for the edge AI explosion.

The applications are vast and growing. Drones can achieve dramatically longer flight times with lighter batteries. Wearable devices gain extended battery life without compromising functionality. Agricultural equipment benefits from real-time monitoring without frequent recharging. Industrial machinery can be equipped with predictive maintenance capabilities that identify subtle anomalies in vibration, temperature, or current draw before failures occur. Robotics systems gain critical safety features through microsecond decision-making capabilities.

For developers, EMAS has prioritized accessibility by ensuring compatibility with familiar frameworks like TensorFlow and PyTorch. Their backend engine handles the translation to optimized binaries, eliminating the learning curve typically associated with specialized hardware.

Ready to experience this breakthrough technology? EMAS offers development kits for hands-on testing and even provides remote access to their hardware for preliminary evaluation. See them in person at upcoming industry events in Amsterdam and Taipei, where they'll showcase how their innovative approach is redefining what's possible with battery-powered intelligent devices.

Join the edge AI revolution and discover how EMAS is making efficient intelligence accessible everywhere it matters.

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"Do you trust your AI models? Honestly, I don't trust them. We should not trust them." These powerful words from STMicroelectronics' Mounia Kharbouche perfectly capture the security challenge facing the edge AI world today.

As organizations rush to deploy AI workloads at the edge, a complex security landscape emerges that demands careful navigation. This fascinating panel discussion dives deep into the three major threat vectors organizations must prepare for: algorithmic attacks that manipulate model behavior, physical attacks on hardware, and side-channel analysis that can steal proprietary models in mere hours.

Through vivid examples—like specially designed glasses that can fool facial recognition systems—the panelists demonstrate how seemingly minor vulnerabilities can lead to major security breaches. They explore the security paradox of edge deployment: while distributing AI provides resilience against single points of failure, it simultaneously creates numerous potential attack surfaces requiring protection.

The conversation reveals a critical tension between economics and security that often drives deployment decisions. Organizations frequently prioritize cost considerations over comprehensive security measures, sometimes with devastating consequences. All panelists emphasize that security must be a fundamental consideration from the beginning of any AI project, not an afterthought tacked on at deployment.

Looking to the future, the discussion turns to emerging threats like agentic AI, where autonomous agents might access resources without proper security constraints. The panel concludes with a sobering examination of post-quantum cryptography and why organizations must prepare now for threats that may not materialize for years but will target systems deployed today.

Whether you're developing edge AI solutions or implementing them in your organization, this discussion provides essential insights for securing your systems against current and future threats. Join us to discover how to balance innovation with protection in the rapidly evolving world of edge AI.

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The humble printer - that device gathering dust in the corner of your office - is about to undergo a remarkable transformation. Thanks to advancements in generative AI, printers and scanners are evolving from passive endpoints into intelligent document processing powerhouses.

Arniban from Wipro Limited unveils how visual language models (VLMs) like QN 2.5 VL and LayoutLMv3 are being deployed directly on edge devices rather than in the cloud. This breakthrough approach addresses critical data privacy concerns while eliminating the need for continuous network connectivity - perfect for sensitive enterprise environments where document security is paramount.

These multimodal AI implementations enable remarkable capabilities that were previously impossible. Imagine a printer that can automatically extract complex tables from documents and convert them into visually appealing charts. Or one that can intelligently correct errors, translate content between languages, adapt layouts for visually impaired users, or even remove advertisements when printing web pages - all without sending your data to external servers.

The technical implementation involves clever optimizations to run these sophisticated models on relatively constrained hardware. Through techniques like 4-bit quantization, image downscaling, and leveraging NVIDIA's optimized libraries, these models can function effectively on devices with 16GB of GPU memory - bringing AI intelligence directly to the point where documents are produced.

While challenges remain in handling large documents and managing the thermal constraints of embedded devices, this technology marks the beginning of a new era in intelligent document processing. The days of printers as "dumb" input-output machines are numbered. The future belongs to intelligent endpoints that understand what they're printing and can transform it in ways that add tremendous value to users.

Try imagining what your workflow could look like when your printer becomes your intelligent document assistant. The possibilities are just beginning to unfold.

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The technological revolution sparked by generative AI threatens to create the deepest digital divide we've ever seen. In this illuminating talk, Danilo Pau from STMicroelectronics reveals how only a handful of companies worldwide possess the resources to fully harness large-scale generative AI, while the rest of humanity risks being left behind.

Pau takes us through the sobering reality of today's AI landscape: hyperparameterized models requiring nuclear power plants for training, hundreds of millions in costs, and worrying environmental impacts. But rather than accept this centralized future, he presents a compelling alternative path – bringing generative AI to edge devices.

Through a comprehensive survey of recent research, Pau demonstrates that generative AI is already running on edge devices ranging from smartphones to microcontrollers. His team's work with STMicroelectronics processors showcases practical implementations including style transfer, language models, and perhaps most impressively, an intelligent thermostat capable of natural language interaction with reasoning capabilities.

What emerges is a vision for AI not as another backend classifier but as a transformative interface between humans and machines. "GenAI is not for another detector," Pau explains. "We need to offer new added value" through natural interactions that understand context and can reason about the world.

For researchers and developers, this talk provides concrete pathways to explore: from audio processing as a "low-hanging fruit" to visual question answering systems that run on minimal hardware. The future of AI isn't just in massive data centers – it's in the devices all around us, waiting to be unleashed through energy-efficient processing and innovative approaches to model optimization.

Ready to join the movement bringing AI capabilities to everyone? Explore how edge-based generative AI could transform your products and help bridge the growing digital divide.

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What happens when trillions of AI agents can discover, communicate, and collaborate across organizational boundaries? Pradyumna Shari from MIT Media Lab unveils NANDA (Networked AI Agents in a Decentralized Architecture), a groundbreaking open protocol that could fundamentally transform how we interact with artificial intelligence.

Drawing a fascinating parallel between computing history and our AI trajectory, Pradyumna explains how we've evolved from isolated large language models to action-capable agents that can reason and act in the world. Yet despite this progress, we're still missing the crucial infrastructure that would allow these agents to find and collaborate with each other across organizational boundaries – essentially, an "Internet of AI Agents."

Using a relatable birthday party planning scenario, Pradyumna demonstrates how interconnected agents could effortlessly coordinate calendars, groceries, and bakery orders without human micromanagement. But enabling this vision requires solving complex challenges around agent discovery, authentication, verifiability, and privacy that differ significantly from traditional web architecture.

At the heart of NANDA's approach is a three-layer registry system designed specifically for dynamic, peer-to-peer agent interactions. The demonstration showcases how this architecture enables diverse communications – from personal agents that adapt messages between family members to commercial interactions between customers and businesses, all while supporting different communication protocols like Google's A2A and Anthropic's MCP.

What makes NANDA particularly exciting is its commitment to democratic, open-source development. Rather than dictating standards, the project invites collaboration from academic and industry partners to build this agent ecosystem together, ensuring it remains transparent, trustworthy, and accessible to all.

Visit nanda.mit.edu to learn more about how you can contribute to this vision of a decentralized, collaborative future for artificial intelligence.

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Stepping into a future where AI doesn't require the cloud, NXP is revolutionizing edge computing by bringing generative AI directly to microprocessors. Alberto Alvarez offers an illuminating journey through NXP's approach to private, secure, and efficient AI inference that operates entirely at the edge.

The heart of NXP's innovation is their EAQ GenAI Flow, a comprehensive software pipeline designed for iMX SoCs that enables both fine-tuning and optimization of AI models. This dual capability allows developers to adapt openly available Large Language Models for specific use cases without compromising data privacy, while also tackling the challenge of memory footprint through quantization techniques that maintain model accuracy. The conversational AI implementation creates a seamless experience by combining wake word detection, speech recognition, language processing with retrieval-augmented generation, and natural speech synthesis—all accelerated by NXP's Neutron NPU.

Most striking is NXP's partnership with Kinara, which introduces truly groundbreaking multimodal AI capabilities running entirely at the edge. Their demonstration of the LAVA model—combining LLAMA3's 8 billion parameters with CLIP vision encoding—showcases the ability to process both images and language queries without any cloud connectivity. Imagine industrial systems analyzing visual scenes, detecting subtle anomalies like water spills, and providing spoken reports—all while keeping sensitive data completely private. With quantization reducing these massive models to manageable 4-bit and 8-bit precision, NXP is making previously impossible edge AI applications practical reality.

Ready to experience the future of edge intelligence? Explore NXP's application code hub to start building with EIQ GenAI resources on compatible hardware and discover how your next project can harness the power of generative AI without surrendering privacy or security to the cloud.

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The boundary between humans and computers is blurring as AI capabilities advance, creating opportunities for more natural, conversational interactions with our devices. Raymond Lowe from Intel takes us on a journey through the evolution of human-computer interaction, from simple mouse clicks to sophisticated chatbots that understand context, process images, and engage in meaningful dialogue.

At the heart of this transformation is OpenVINO, Intel's toolkit for optimizing neural networks across diverse hardware. Raymond demonstrates how this technology enables edge devices—from laptops to specialized processors—to run sophisticated AI models locally without requiring cloud connectivity. The examples are compelling: generating beautiful images of teddy bears in just seconds on standard laptop GPUs, running large language models that once consumed 25GB of RAM on modest hardware, and creating smart cameras that can describe what your baby is doing without complex coding.

Memory management emerges as the hero of this story. Through techniques like quantization (reducing model precision from 32-bit to 8-bit or even 4-bit), OpenVINO dramatically shrinks model size while maintaining accuracy. This isn't just about fitting models into limited memory—it's about activating specialized hardware instructions that can deliver 2-3x performance improvements, transforming sluggish experiences into fluid, real-time interactions.

The impact extends beyond technical achievements. Raymond shares the emotional moment when he first got a chatbot running locally: "I never felt so alive when I saw the machine talking to me." For developers, this means being able to create prototypes in weeks rather than months, accessing hundreds of pre-optimized examples, and focusing on building experiences rather than struggling with technical hurdles.

Through partnerships with Microsoft's AI Foundry program, these capabilities are being integrated directly into Windows, ensuring consumers get optimal AI performance from their hardware without additional setup. For industries embracing AI—from healthcare to retail to smart cities—OpenVINO offers a path to enhance existing applications while exploring new possibilities at the intersection of traditional and generative AI approaches.

Want to experience this revolution yourself? Check out Intel's extensive library of notebooks and examples, or try the Open Edge Platform to start building immediately. The future of human-computer interaction isn't just coming—it's already here on your local device.

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The growing gap between rapidly evolving AI models and lagging deployment frameworks creates a significant challenge for edge AI developers. Maurice Sersiff, CEO and co-founder of Germany-based Roofline AI, presents a compelling solution to this problem through innovative compiler technology designed to make edge AI deployment simple and efficient.

At the heart of Roofline's approach is a retargetable AI compiler that acts as the bridge between any AI model and diverse hardware targets. Their SDK supports all major frameworks (PyTorch, TensorFlow, ONNX) and model architectures from traditional CNNs to cutting-edge LLMs. The compiler generates optimized code specifically tailored to the target hardware, whether it's multi-core ARM systems, embedded GPUs, or specialized NPUs.

What truly sets Roofline apart is their unwavering commitment to comprehensive model coverage. They operate with a "day zero support" philosophy—if a model doesn't work, that's considered a bug to be fixed within 24 hours. This approach enables developers to use the latest models immediately without waiting months for support. Performance benchmarks demonstrate the technology delivers 1-3x faster execution speeds compared to alternatives like Torch Inductor while significantly reducing memory footprint.

Maurice provides a fascinating comparison between Roofline's compiler-based approach for running LLMs on edge devices versus the popular library-based solution LLama.cpp. While hand-optimized kernels currently maintain a slight performance edge, Roofline offers vastly superior flexibility and immediate support for new models. Their ongoing optimization work is rapidly closing the performance gap, particularly on ARM platforms.

Interested in simplifying your edge AI deployment while maintaining performance? Explore how Roofline AI's Python-integrated SDK can help you bring any model to any chip with minimal friction, enabling true innovation at the edge.

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