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.
XR Tech Market Report
Woodside Capital Partners (WCP) is pleased to share its XR Tech Market Report, authored by senior bankers Alain Bismuth and Rudy Burger, and by analyst Alex Bonilla. Why we are interested in the XR Ecosystem Investors have been pouring billions of dollars into developing enabling technologies for augmented reality (AR) glasses aimed at the consumer market,
Boosting Data Quality: Simulation-based vs. Generative AI Synthetic Data Generation
This blog post was originally published at Geisel Software’s Symage website. It is reprinted here with the permission of Geisel Software. Imagine you’re tasked with boosting data quality for your AI model. You’re at a crossroads, faced with two distinct paths for generating synthetic image data. On one side, there’s Generative AI—fast, adaptable, and capable
The Era of Physical AI is Here
This blog post was originally published at SiMa.ai’s website. It is reprinted here with the permission of SiMa.ai. The AI landscape is undergoing a monumental shift. After a decade where AI flourished in the cloud, scaled by hyperscalers, we are now entering the era of Physical AI. Physical AI is poised to touch every facet
OpenAI’s gpt-oss-20b: Its First Open-source Reasoning Model to Run on Devices with Snapdragon
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. At Qualcomm Technologies, we’ve long believed that AI assistants will be ubiquitous, personal and on-device. Today, we’re excited to share a major milestone in that journey: OpenAI has open-sourced its first reasoning model, gpt-oss-20b, a chain-of-thought reasoning
“Visual Search: Fine-grained Recognition with Embedding Models for the Edge,” a Presentation from Gimlet Labs
Omid Azizi, Co-Founder of Gimlet Labs, presents the “Visual Search: Fine-grained Recognition with Embedding Models for the Edge” tutorial at the May 2025 Embedded Vision Summit. In the domain of AI vision, we have seen an explosion of models that can reliably detect objects of various types, from people to… “Visual Search: Fine-grained Recognition with
“Optimizing Real-time SLAM Performance for Autonomous Robots with GPU Acceleration,” a Presentation from eInfochips
Naitik Nakrani, Solution Architect Manager at eInfochips, presents the “Optimizing Real-time SLAM Performance for Autonomous Robots with GPU Acceleration” tutorial at the May 2025 Embedded Vision Summit. Optimizing execution time of long-term and large-scale SLAM algorithms is essential for real-time deployments on edge compute platforms. Faster SLAM output means faster… “Optimizing Real-time SLAM Performance for
SiMa.ai Next-Gen Platform for Physical AI in Production
Modalix in Production, Now Shipping SoM Pin-Compatible with leading GPU SoM, Dev Kits, and LLiMa for Seamless LLM-to-Modalix Integration SAN JOSE, Calif., August 12, 2025 — SiMa.ai, a pioneer in Physical AI solutions, today is making three significant product announcements to accelerate the scaling of Physical AI. Production and immediate availability of its next-generation Physical
R²D²: Boost Robot Training with World Foundation Models and Workflows from NVIDIA Research
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. As physical AI systems advance, the demand for richly labeled datasets is accelerating beyond what we can manually capture in the real world. World foundation models (WFMs), which are generative AI models trained to simulate, predict, and
