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.

“Scaling Machine Learning with Containers: Lessons Learned,” a Presentation from Instrumental

Rustem Feyzkhanov, Machine Learning Engineer at Instrumental, presents the “Scaling Machine Learning with Containers: Lessons Learned” tutorial at the May 2025 Embedded Vision Summit. In the dynamic world of machine learning, efficiently scaling solutions from research to production is crucial. In this presentation, Feyzkhanov explores the nuances of scaling machine… “Scaling Machine Learning with Containers:

Read More »

“Vision-language Models on the Edge,” a Presentation from Hugging Face

Cyril Zakka, Health Lead at Hugging Face, presents the “Vision-language Models on the Edge” tutorial at the May 2025 Embedded Vision Summit. In this Zakka, we provides an overview of vision-language models (VLMs) and their deployment on edge devices using Hugging Face’s recently released SmolVLM as an example. He examines… “Vision-language Models on the Edge,”

Read More »

OwLite Meets Qualcomm Neural Network: Unlocking On-device AI Performance

This blog post was originally published at SqueezeBits’ website. It is reprinted here with the permission of SqueezeBits. At SqueezeBits we have been empowering developers to efficiently deploy complex AI models while minimizing performance trade-offs with OwLite toolkit. With OwLite v2.5, we’re excited to announce official support for Qualcomm Neural Network (QNN) through seamless integration

Read More »

“Vision LLMs in Multi-agent Collaborative Systems: Architecture and Integration,” a Presentation from Google

Niyati Prajapati, ML and Generative AI Lead at Google, presents the “Vision LLMs in Multi-agent Collaborative Systems: Architecture and Integration” tutorial at the May 2025 Embedded Vision Summit. In this talk, Prajapati explores how vision LLMs can be used in multi-agent collaborative systems to enable new levels of capability and… “Vision LLMs in Multi-agent Collaborative

Read More »

Shifting AI Inference from the Cloud to Your Phone Can Reduce AI Costs

This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. Every AI query has a cost, and not just in dollars. Study shows distributing AI workloads to your devices — such as your smartphone — can reduce costs and decrease water consumption What you should know: Study

Read More »

“Building Agentic Applications for the Edge,” a Presentation from GMAC Intelligence

Amit Mate, Founder and CEO of GMAC Intelligence, presents the “Building Agentic Applications for the Edge” tutorial at the May 2025 Embedded Vision Summit. Along with AI agents, the new generation of large language models, vision-language models and other large multimodal models are enabling powerful new capabilities that promise to… “Building Agentic Applications for the

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.

Contact

Address

Berkeley Design Technology, Inc.
PO Box #4446
Walnut Creek, CA 94596

Phone
Phone: +1 (925) 954-1411
Scroll to Top