This blog post was originally published at AMD’s website. It is reprinted here with the permission of AMD. This blog showcases how to deploy power-efficient Ryzen AI perception models with ROS 2 – the Robot Operating System. We utilize the Ryzen AI Max+ 395 (Strix-Halo) platform, which is equipped with an efficient Ryzen AI NPU and […]
Building Robotics Applications with Ryzen AI and ROS 2 Read More +
This blog post was originally published at Axelera AI’s website. It is reprinted here with the permission of Axelera AI. A camera sees everything and understands nothing. For decades, that has been the fundamental limitation of physical security at scale: vast amounts of footage, limited ability to act on it in real time. The gap between
The Future of Security Is Already Running. Here Is What It Looks Like. Read More +
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. The Gemmaverse expands with the launch of the latest Gemma 4 multimodal and multilingual models, designed to scale across the full spectrum of deployments, from NVIDIA Blackwell in the data center to Jetson at the edge. These models are suited
Bringing AI Closer to the Edge and On-Device with Gemma 4 Read More +
MOUNTAIN VIEW, Calif., April 2, 2026 — Google has introduced Gemma 4, a new family of open models with open weights that is clearly aimed at bringing more capable AI onto local hardware. Released under the Apache 2.0 license, the Gemma 4 family includes four sizes: E2B, E4B, 26B A4B MoE and 31B Dense. Google
Google Pushes Multimodal AI Further Onto Edge Devices with Gemma 4 Read More +
This blog post was originally published at Quadric’s website. It is reprinted here with the permission of Quadric. After years of cloud-centric inference, AI is moving to the edge. The “Goldilocks zone” of 3B to 30B parameter models is delivering GPT-4-class performance on smartphones, automotive systems, and industrial equipment — and creating an acute challenge for
The On-Device LLM Revolution: Why 3B-30B Models Are Moving to the Edge Read More +
This blog post was originally published at Samsung Semiconductor’s website. It is reprinted here with the permission of Samsung Semiconductor. Until recently, the evolution of mobile cameras has been centered on the image sensor and the image signal processor (ISP). In this conventional architecture, the image sensor converts light into electrical signals, while the ISP corrects
Inside the Intelligent Mobile Camera Powered by Exynos 2600 VPS Read More +
On April 16, 2026, at 1:00 pm EDT (10:00 am PDT) Boston.AI will deliver a webinar “Remembering to Forget: Agentic Memory Systems and Context Constraints” From the event page: As AI agents evolve from stateless responders into persistent, goal-directed systems, memory has become a central design challenge. The question is no longer just what agents
Upcoming Webinar on Agentic Memory Systems Read More +
Microchip presents a customizable, target-agnostic solution to program wake words and voice commands. The ML model, generated and tested using a custom application, has low latency and a minimal memory footprint, making it ideal for resource-constrained embedded systems. The ML model can be integrated into voice-based applications running on any 32-bit microcontroller or microprocessor running
Lightweight Keyword Spotting Solution from Microchip Read More +
This article was originally published at Analog Devices’ website. It is reprinted here with the permission of Analog Devices. Artificial intelligence is entering a new phase where models interpret contextual data whilst interacting with the physical world in real time. At Analog Devices, Inc. (ADI), we call this Physical Intelligence: intelligent systems that can perceive, reason
2026: The Year Intelligence Gets Physical Read More +
Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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