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.
Free Webinar Explores Edge AI-enabled Microcontroller Capabilities and Trends
On November 18, 2025 at 9 am PT (noon ET), the Yole Group’s Tom Hackenberg, principal analyst for computing, will present the free hour webinar “How AI-enabled Microcontrollers Are Expanding Edge AI Opportunities,” organized by the Edge AI and Vision Alliance. Here’s the description, from the event registration page: Running AI inference at the edge,
“Lessons Learned Building and Deploying a Weed-killing Robot,” a Presentation from Tensorfield Agriculture
Xiong Chang, CEO and Co-founder of Tensorfield Agriculture, presents the “Lessons Learned Building and Deploying a Weed-Killing Robot” tutorial at the May 2025 Embedded Vision Summit. Agriculture today faces chronic labor shortages and growing challenges around herbicide resistance, as well as consumer backlash to chemical inputs. Smarter, more sustainable approaches… “Lessons Learned Building and Deploying
Using the Qualcomm AI Inference Suite from Google Colab
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. Building off of the blog post here, which shows how easy it is to call the Cirrascale AI Inference Cloud using the Qualcomm AI Inference Suite, we’ll use Google Colab to show the same scenario. In the previous blog
“Transformer Networks: How They Work and Why They Matter,” a Presentation from Synthpop AI
Rakshit Agrawal, Principal AI Scientist at Synthpop AI, presents the “Transformer Networks: How They Work and Why They Matter” tutorial at the May 2025 Embedded Vision Summit. Transformer neural networks have revolutionized artificial intelligence by introducing an architecture built around self-attention mechanisms. This has enabled unprecedented advances in understanding sequential… “Transformer Networks: How They Work
Qualcomm to Acquire Arduino—Accelerating Developers’ Access to its Leading Edge Computing and AI
New Arduino UNO Q and Arduino App Lab to Enable Millions of Developers with the Power of Qualcomm Dragonwing Processors Highlights: Acquisition to combine Qualcomm’s leading-edge products and technologies with Arduino’s vast ecosystem and community to empower businesses, students, entrepreneurs, tech professionals, educators and enthusiasts to quickly and easily bring ideas to life. New Arduino
Deploy High-performance AI Models in Windows Applications on NVIDIA RTX AI PCs
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Microsoft is now making Windows ML available to developers. Windows ML enables C#, C++ and Python developers to optimally run AI models locally across PC hardware from CPU, NPU and GPUs. On NVIDIA RTX GPUs, it utilizes
“Understanding Human Activity from Visual Data,” a Presentation from Sportlogiq
Mehrsan Javan, Chief Technology Officer at Sportlogiq, presents the “Understanding Human Activity from Visual Data” tutorial at the May 2025 Embedded Vision Summit. Activity detection and recognition are crucial tasks in various industries, including surveillance and sports analytics. In this talk, Javan provides an in-depth exploration of human activity understanding,… “Understanding Human Activity from Visual
Andes Technology Expands Comprehensive AndeSentry Security Suite with Complete Trusted Execution Environment Support for Embedded Systems
Includes IOPMP, Secure Boot, MCU-TEE for RTOS, and OP-TEE for Linux to Protect Devices from MCUs to Edge AI Processors Hsinchu, Taiwan – October 6th, 2025 – Andes Technology Corporation, the leading supplier of high-efficiency, low-power 32/64-bit RISC-V processor cores, today announced the latest AndeSentry™ Framework with two new components, Secure Boot v1.0.1 and MCU-TEE
