Energy Efficient and high throughput inference using compressed tsetlin machine
Logic beats arithmetic in the machine learning revolution happening at Newcastle University. From a forgotten Soviet mathematician's work in the 1960s to modern embedded systems, Settle Machine represents a paradigm shift in how we approach artificial intelligence.Unlike traditional neural networks that rely on complex mathematical operations, Settle Machine harnesses Boolean logic - simple yes/no questions similar to how humans naturally think. This "white box" approach creates interpretable models using only AND gates, OR gates, and NOT gates without any multiplication operations. The result? Machine learning that's not only understandable but dramatically more efficient.The technical magic happens through a process called Booleanization, converting input data into binary questions that feed learning automata. These finite state machines work in parallel, creating logical patterns that combine to make decisions. What's remarkable is the natural sparsity of the resulting models - for complex tasks like image recognition, more than 99% of potential features are automatically excluded. By further optimizing this sparsity and removing "weak includes," Newcastle's team has achieved astonishing efficiency improvements.The numbers don't lie: 10x faster inference time than Binarized Neural Networks, dramatically lower memory footprint, and energy efficiency improvements around 20x on embedded platforms. Their latest microchip implementation consumes just 8 nanojoules per frame for MNIST character recognition - likely the lowest energy consumption ever published for this benchmark. For edge computing and IoT applications where power constraints are critical, this breakthrough opens new possibilities.Beyond efficiency, Settle Machine addresses the growing demand for explainable AI. As regulations tighten around automated decision-making, the clear logical propositions generated by this approach provide transparency that black-box neural networks simply can't match. Ready to explore this revolutionary approach? Visit settlemachine.org or search for the unified GitHub repository to get started with open-source implementations.Send us a textSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
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Applying GenAI to Mice Monitoring
The AI revolution isn't just for tech giants with unlimited computing resources. Small and medium enterprises represent a crucial frontier for edge generative AI adoption, but they face unique challenges when implementing these technologies. This fascinating exploration takes us into an unexpected application: smart laboratory mouse cages enhanced with generative AI.Laboratory mice represent valuable assets in pharmaceutical research, with their welfare being a top priority. While fixed-function AI already monitors basic conditions like water and food availability through camera systems, the next evolution requires predicting animal behavior and intentions. By analyzing just 16 frames of VGA-resolution video, this edge-based system can predict a mouse's next actions, potentially protecting animals from harm when human intervention isn't immediately possible due to clean-room protocols.The technical journey demonstrates how generative AI can be scaled appropriately for edge devices. Starting with a 240-million parameter model (far smaller than headline-grabbing LLMs), the team optimized to 170 million parameters while actually improving accuracy. Running on a Raspberry Pi 5 without hardware acceleration, the system achieves inference times under 300 milliseconds – and could potentially reach real-time performance (30ms) with specialized hardware. The pipeline combines three generative neural networks: a video-to-my model, an OPT transformer, and a text-to-speech component for natural interaction.This case study provides valuable insights for anyone looking to implement edge generative AI in resource-constrained environments. While currently limited to monitoring single mice, the approach demonstrates that meaningful AI applications don't require supercomputers or billion-parameter models – opening doors for businesses of all sizes to harness generative AI's potential.Send us a textSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
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Simple Cost Effective Vision AI Solutions at the edge
Sony's revolutionary IMX500 stands at the forefront of a quiet revolution in edge computing and smart city technology. This isn't just another image sensor—it's the first to integrate AI processing directly on the chip, transforming how visual data becomes actionable intelligence while preserving privacy and minimizing infrastructure requirements.The power of this innovation lies in its elegant simplicity. Rather than sending complete images to cloud servers or external GPUs for processing, the IMX500 performs AI inference locally and transmits only the resulting metadata. This approach slashes bandwidth requirements to mere kilobytes, dramatically reduces power consumption, and—perhaps most critically—protects individual privacy by ensuring that identifiable images never leave the device. For urban environments where surveillance concerns often clash with safety imperatives, this represents a breakthrough compromise.Real-world deployments already demonstrate the technology's transformative potential. In Lakewood, Colorado, where a one-mile stretch of road had become notorious for traffic fatalities, Sony's solution achieved 100% performance in identifying dangerous situations—outperforming three competing technologies while costing less. Through partnership with ITRON, these sensors can be seamlessly deployed using existing streetlight infrastructure, creating mesh networks of intelligent sensors without requiring expensive new installation work or dedicated power sources. This practical approach to deployment makes citywide implementation financially viable even for budget-constrained municipalities.The implications extend far beyond traffic monitoring. From retail analytics to manufacturing quality control, the same core technology can be applied wherever visual intelligence provides value. By bringing AI to the edge in a form factor that addresses privacy, power, and practical deployment challenges, Sony has created a foundation for the next generation of smart infrastructure. Explore how this technology could transform your environment—whether an urban center, commercial space, or industrial facility—by leveraging the power of visual intelligence without the traditional limitations.Send us a textSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
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Low Code No Code Platform for Developing AI algorithms
Revolutionizing edge computing just got easier. This eye-opening exploration of ST Microelectronics' ST-IoT Craft platform reveals how everyday developers can now harness the power of artificial general intelligence without writing a single line of code.The modern IoT landscape presents a paradox: billions of devices generate zettabytes of valuable data, yet transforming that raw information into intelligent systems remains frustratingly complex. ST's innovative low-code/no-code platform elegantly solves this problem by distributing intelligence across three key components: smart sensors with embedded AI algorithms, intelligent gateways that filter data transmission, and cloud services that handle model training and adaptation.At the heart of this revolution is truly remarkable in-sensor AI technology. Imagine sensors that don't just collect data but actually think – detecting whether a laptop is on a desk or in a bag, whether an industrial asset is stationary or being handled, or whether a person is walking or running. These decisions happen directly on the sensor itself, dramatically reducing power consumption and network traffic while enabling real-time responses. The platform offers 31 different features including mean, variance, energy in bands, peak-to-peak values, and zero crossing that can be automatically selected and applied to your data.What makes ST-IoT Craft truly accessible is its browser-based interface with six pre-built examples spanning industrial and consumer applications. Users can visualize sensor data in real-time, train models with a single button click, and deploy finished solutions directly to hardware – all without diving into complex code. The platform even handles the intricate details of filter selection, feature extraction, window length optimization, and decision tree generation automatically.Ready to transform your IoT projects with embedded intelligence? Visit stcom, search for ST-IoT Craft, and discover how you can teach your sensors to think – no coding required.Send us a textSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
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Stochastic Training for Side-Channel Resilient AI
Protecting valuable AI models from theft is becoming a critical concern as more computation moves to edge devices. This fascinating exploration reveals how sophisticated attackers can extract proprietary neural networks directly from hardware through side-channel attacks - not as theoretical possibilities, but as practical demonstrations on devices from major manufacturers including Nvidia, ARM, NXP, and Google's Coral TPUs.The speakers present a novel approach to safeguarding existing hardware without requiring new chip designs or access to proprietary compilers. By leveraging the inherent randomness in neural network training, they demonstrate how training multiple versions of the same model and unpredictably switching between them during inference can significantly reduce vulnerability to these attacks.Most impressively, they overcome the limitations of edge TPUs by cleverly repurposing ReLU activation functions to emulate conditional logic on hardware that lacks native support for control flow. This allows implementation of security measures on devices that would otherwise be impossible to modify. Their technique achieves approximately 50% reduction in side-channel leakage with minimal impact on model accuracy.The presentation walks through the technical implementation details, showing how layer-wise parameter selection can provide quadratic security improvements compared to whole-model switching approaches. For anyone working with AI deployment on edge devices, this represents a critical advancement in protecting intellectual property and preventing system compromise through model extraction.Try implementing this stochastic training approach on your edge AI systems today to enhance security against physical attacks. Your valuable AI models deserve protection as they move closer to end users and potentially hostile environments.Send us a textSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
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