EDGE AI POD

The landscape of artificial intelligence is experiencing a profound transformation, with AI capabilities moving from distant cloud servers directly to edge devices where your data lives. This pivotal shift isn't just about running small models locally—it represents a fundamental reimagining of how we interact with AI systems.

In this fascinating exploration, Dell Technologies' Aruna Kolluru takes us deep into the world of edge-deployed AI agents that can perceive their surroundings, generate language, plan actions, remember context, and use tools—all without requiring cloud connectivity. These aren't simple classification systems but fully autonomous digital partners capable of making complex decisions where your data is generated.

Discover how miniaturized foundation models like Mistral and TinyLlama, combined with agentic frameworks and edge-native runtimes, have made this revolution possible. Through compelling real-world examples, Aruna demonstrates how these systems are transforming industries today: autonomous factory agents detecting defects and triggering interventions, rural healthcare assistants providing offline medical guidance, disaster response drones generating situational awareness, and personalized retail advisors creating real-time offers for shoppers.

The technical journey doesn't stop at deployment. We examine the sophisticated optimization techniques making these models edge-friendly, the memory systems enabling contextual awareness, and the planning frameworks orchestrating multi-step workflows. Importantly, we tackle the critical governance considerations for these autonomous systems, including encrypted storage, tool access control, and comprehensive audit logging.

Whether you're a developer looking to build edge AI solutions, an enterprise decision-maker exploring AI deployment options, or simply curious about where AI is headed, this episode offers invaluable insights into a technology that's bringing intelligence directly to where it's needed most. Subscribe to our podcast and join the conversation about the future of AI at the edge!
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When personal tragedy strikes, some find a way to transform pain into purpose. Such is the remarkable story behind Ambient Scientific, where founder GP Singh's mission to prevent falls after losing a family member evolved into groundbreaking semiconductor technology enabling AI at the ultra-low power edge.

The journey wasn't simple. Creating chips that could run sophisticated deep learning algorithms on tiny batteries proved more challenging than building data center processors. This demanded innovation at every level – from custom instruction sets and compilers to complete software stacks. What emerged wasn't just a single-purpose chip but a programmable platform with the versatility to support diverse applications while consuming a fraction of the power of conventional solutions.

Most fascinating is what GP calls the "gravitational pull" toward edge computing. Applications initially deployed in the cloud inevitably migrate closer to where data originates – from data centers to on-premises, to desktops, to mobile devices, and ultimately to tiny wearables. This migration stems from fundamental business concerns: operating costs, data sovereignty, vendor lock-in, and the inherent distrust organizations have for cloud dependencies. The evidence? In hundreds of customer conversations, GP has yet to meet a single organization content with keeping their AI exclusively in the cloud.

Ready to explore ultra-low power AI? Ambient Scientific offers development kits accessible to anyone familiar with embedded systems programming and Python-based deep learning. Join the revolution bringing intelligence to where data is created, not where it's processed. Your next innovation might be powered by a chip that sips power while delivering remarkable AI capabilities.
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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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