Development Tools

Development Tools for Embedded Vision

ENCOMPASSING MOST OF THE STANDARD ARSENAL USED FOR DEVELOPING REAL-TIME EMBEDDED PROCESSOR SYSTEMS

The software tools (compilers, debuggers, operating systems, libraries, etc.) encompass most of the standard arsenal used for developing real-time embedded processor systems, while adding in specialized vision libraries and possibly vendor-specific development tools for software development. On the hardware side, the requirements will depend on the application space, since the designer may need equipment for monitoring and testing real-time video data. Most of these hardware development tools are already used for other types of video system design.

Both general-purpose and vender-specific tools

Many vendors of vision devices use integrated CPUs that are based on the same instruction set (ARM, x86, etc), allowing a common set of development tools for software development. However, even though the base instruction set is the same, each CPU vendor integrates a different set of peripherals that have unique software interface requirements. In addition, most vendors accelerate the CPU with specialized computing devices (GPUs, DSPs, FPGAs, etc.) This extended CPU programming model requires a customized version of standard development tools. Most CPU vendors develop their own optimized software tool chain, while also working with 3rd-party software tool suppliers to make sure that the CPU components are broadly supported.

Heterogeneous software development in an integrated development environment

Since vision applications often require a mix of processing architectures, the development tools become more complicated and must handle multiple instruction sets and additional system debugging challenges. Most vendors provide a suite of tools that integrate development tasks into a single interface for the developer, simplifying software development and testing.

The architecture shift powering next-gen industrial AI

This blog post was originally published at Arm’s website. It is reprinted here with the permission of Arm. How Arm is powering the shift to flexible AI-ready, energy-efficient compute at the “Industrial Edge.” Industrial automation is undergoing a foundational shift. From industrial PC to edge gateways and smart sensors, compute needs at the edge are changing fast. AI is moving

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NVIDIA Advances Open Model Development for Digital and Physical AI

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. NVIDIA releases new AI tools for speech, safety and autonomous driving — including NVIDIA DRIVE Alpamayo-R1, the world’s first open industry-scale reasoning vision language action model for mobility — and a new independent benchmark recognizes the openness and

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OpenVINO 2025.4 Release Broadens Model Support

OpenVINO 2025.4 is very much an edge-first release: it tightens the loop between perception, language, and action across AI PCs, embedded devices, and near-edge servers. On the model side, Intel is clearly optimizing for “local RAG + agents.” CPUs and GPUs now get first-class support for Qwen3-Embedding-0.6B and Qwen3-Reranker-0.6B, plus Mistral-Small-24B-Instruct-2501, giving developers a compact

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Breaking the Human Accuracy Barrier in Computer Vision Labeling

This article was originally published at 3LC’s website. It is reprinted here with the permission of 3LC. Introduction There’s been a lot of excitement lately around how foundation models (such as CLIP, SAM, Grounding DINO, etc.) can come close to human-level performance when labeling common objects, saving a ton of labeling effort and cost. It’s impressive progress. However,

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NVIDIA and Synopsys Announce Strategic Partnership to Revolutionize Engineering and Design

Key Highlights Multiyear collaboration spans NVIDIA CUDA accelerated computing, agentic and physical AI, and Omniverse digital twins to achieve simulation speed and scale previously unattainable through traditional CPU computing — opening new market opportunities across engineering. To further adoption of GPU-accelerated engineering solutions, the companies will collaborate in engineering and marketing activities. NVIDIA invested $2

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Why Edge AI Struggles Towards Production: The Deployment Problem

There is no shortage of articles about how to develop and train Edge AI models. The community has also written extensively about why it makes sense to run those models at the edge: to reduce latency, preserve privacy, and lower data-transfer costs. On top of that, the MLOps ecosystem has matured quickly, providing the pipelines

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Nota AI Signs Technology Collaboration Agreement with Samsung Electronics for Exynos AI Optimization “Driving the Popularization of On-Device Generative AI”

Nota AI’s optimization technology integrated into Samsung Electronics’ Exynos AI Studio, enhancing efficiency in on-device AI model development   Seoul, South Korea Nov.26, 2025 — Nota AI, a company specializing in AI model compression and optimization, announced today that it has signed a collaboration agreement with Samsung Electronics’ System LSI Business to provide its AI

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Google Announces LiteRT Qualcomm AI Engine Direct Accelerator

Google has announced a new LiteRT Qualcomm AI Engine Direct Accelerator, giving Android and embedded developers a much more direct path to Qualcomm NPUs for on-device AI and vision workloads. Built on top of Qualcomm’s AI Engine Direct (“QNN”) SDK, the new accelerator replaces the older TensorFlow Lite QNN delegate and plugs directly into LiteRT,

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