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.
Who Decides Edge AI Winners in Embedded?
In data centers, ML researchers’ vote for Nvidia is what made Nvidia a runaway success in AI training. On the embedded market, who holds the key for edge AI? What’s at stake: Edge AI – or AIOT (Artificial Intelligence of Things) – on the embedded market has been a hot notion among MCU vendors for
What is Interpolation? Understanding Image Perception in Embedded Vision Camera Systems
This blog post was originally published at e-con Systems’ website. It is reprinted here with the permission of e-con Systems. Interpolation is a mathematical technique used to estimate unknown values that lie between known data points. Interpolation helps transform raw sensor data into stunning, full-color images in embedded vision systems. Read the blog to learn
From Generative to Agentic AI, Wrapping the Year’s AI Advancements
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Editor’s note: This post is part of the AI Decoded series, which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for GeForce RTX PC and NVIDIA RTX workstation users.
Spikes are the Next Digits
This article was originally published at Digica’s website. It is reprinted here with the permission of Digica. Remember the anxiety felt back in the 1990s after the publications of the first quantum algorithms by Deutsh and Jozsa (1992), Shor (1994), and Grover (1996). Most of us expected quantum computers to be of practical use within
Qualcomm CEO Cristiano Amon at Web Summit: GenAI is the New UI
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. How generative AI (GenAI)-powered “agents” will change the way you interact with the digital world The rise of artificial intelligence (AI) opens the door to a vast array of possibilities. AI-powered agents will be the key to
An Easy Introduction to Multimodal Retrieval-augmented Generation for Video and Audio
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Building a multimodal retrieval augmented generation (RAG) system is challenging. The difficulty comes from capturing and indexing information from across multiple modalities, including text, images, tables, audio, video, and more. In our previous post, An Easy Introduction
Computer Vision Pipeline v2.0
This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. In the realm of computer vision, a shift is underway. This article explores the transformative power of foundation models, digging into their role in reshaping the entire computer vision pipeline. It also demystifies the hype behind the
Generative AI In the Medical Domain: Not Quite Yet
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. Why data is expensive Data is the bedrock of any AI/ML project, and serves as the vital link between mathematical algorithms and real-world problems. And yet, we often grapple with two common data-related challenges, which are the
