In the modern era of sport, results are often decided by ultra-fine margins like milliseconds or millimeters. Yet even precise metrics such as speed and distance are still quite rudimentary in nature.
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Deploying machine learning models at the edge is crucial for real-time data processing in industrial applications. This article explores how Docker simplifies Edge AI deployment on the Arduino Portenta X8, enabling scalable and secure AI implementations on embedded Linux systems.
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Event-based computing is transforming AI processing, delivering smarter, faster, and hyper-efficient solutions across industries like IoT, healthcare, and automotive.
The Edge AI industry is at a turning point with big shots investing heavily. Now is the time to define best practices, share experiences and crowdsource high quality data. Who will be taking the lead in guiding the industry forward?
2 minutes read
AI and ML-driven cashierless stores not only improve operational efficiency and customer personalization but are also expected to propel the global AI retail market to over $40 billion by 2030.
AI is transforming security through real-time threat detection and intelligent surveillance, with Axelera AI advancing this shift using its Metis AI Processing Unit (AIPU) to enhance proactive, adaptive, and sustainable security solutions.
The guide to understanding the current state of the art in hardware & software for Edge AI.
Missed out latest webinar? Watch it on demand now to stay up to date with all the latest from Generative AI at the Edge.
In an interview from CES 2025, Vikram Gupta, Chief Product Officer at Synaptics, discusses the company's strategic collaboration with Google to enhance Edge AI processors, emphasizing the importance of context-aware computing and AI-native platforms for improving user experience and efficiency.
GPUs excel in parallel processing for graphics and AI training with scalability, while NPUs focus on low-latency AI inference on edge devices, enhancing privacy by processing data locally. Together, they complement each other in addressing different stages of AI workloads efficiently.
In the final chapter of the Edge AI Technology Report: Generative AI Edition explores the technical hurdles organizations face as they attempt to leverage edge-based generative AI. It also examines strategic opportunities for innovation in hardware, deployment configurations, and security measures.
webinar | JAN 15, 2025
Join us for The Rise of Generative AI at the Edge: From Data Centers to Devices webinar on Wednesday, January 15th
Edge AI Technology Report: Generative AI Edition Chapter 3. Advances in model optimization techniques like pruning, quantization, and knowledge distillation pave the way for more efficient deployment of generative AI at the edge.
