RidgeRun Software Developer Blog

Explore the key challenges of embedded vision in robotics and how RidgeRun helps teams integrate cameras, LiDAR, ROS 2, Edge AI, real-time perception, and low-latency teleoperation into production-ready robotic systems.

Embedded vision and Edge AI are transforming medical and healthcare systems by enabling real-time imaging, low-latency streaming, patient privacy protection, and smarter medical device workflows directly at the edge.

As embedded systems and IoT solutions continue to adopt AI-driven capabilities, the demand for powerful and energy-efficient platforms continues to grow.. The Dragonwing IQ-9075 EVK board maximizes the features of the Dragonwing IQ-9075 processor, optimized for industrial IoT, Edge AI, and robotics applications, being able to sustain through demanding workloads and offering a high-performance power efficient computing power, on-device AI with a dedicated NPU, and a camera an

Qualcomm Dragonwing IQ-9075 is an embedded SoC platform designed for demanding edge AI, multimedia, robotics, drone, and industrial vision applications. As part of Qualcomm’s Dragonwing portfolio, it targets industrial, enterprise, and embedded systems that require high-performance processing, power-efficient AI, advanced connectivity, and long-term deployment support. About the board The Dragonwing IQ-9075 platform delivers high-compute, power-efficient AI performance with t

RidgeRun helps industrial automation teams integrate embedded vision, Edge AI, real-time video processing, and sensor systems to improve quality inspection, remote monitoring, predictive maintenance, and smarter decision-making on the production floor.**

Real-time AI at the edge is reshaping industries from medical devices to broadcast media. NVIDIA’s ecosystem—GStreamer, DeepStream, Holoscan SDK, and Holoscan for Media—offers multiple approaches to building streaming AI pipelines, each with distinct trade-offs in latency, scalability, and architecture. This deep dive clarifies how these frameworks differ and when to use each.

The NXP i.MX95 platform is designed to meet the growing demands of edge AI and modern embedded systems, combining heterogeneous processing, a dedicated Neural Processing Unit (NPU), and advanced multimedia capabilities. This article provides a decision-focused overview of its architecture, software ecosystem, and key use cases, helping technical leaders evaluate its suitability for industrial, automotive, and intelligent IoT applications.

Accelerating Deep Learning with FPGAs in a nutshell. Exploring the different architectures and implementations for low-latency inference

Select the best hardware device for AI and deep learning inference acceleration

Reduce sensor capture latency with the Low Latency Holoscan Sensor Bridge

CPU, GPU and FPGA help to perform hardware acceleration. Here, we detail some of the considerations to consider each of them

FPGAs are versatile and powerful devices and outstand the traditional processing circuits like CPUs and GPUs in latency

A research showing the process of automating action detection of an assembly sequence of a hardware part in a real production line

RidgeRun is creating a set of Python reference designs to be used as starting points for applications using deep learning in some form.

RidgeRun is thrilled to announce its new partnership with Coral from Google. Coral is a complete toolkit that allows you to build...

A media server provides multimedia all-in-one features, such as video capture, processing, streaming, recording, and, in some cases, the...

For better understanding of this latest blog from RidgeRun, please read the RidgeRun blog on Diving deep into NVIDIA Jetson TX2 - Video...