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.
This blog post was originally published at SLAMcore’s website. It is reprinted here with the permission of SLAMcore. The three most challenging questions in autonomous robotics are: where am I? how far away are the objects around me? and, what are those objects? The vast majority of robot failures stem from an inability to answer
Provisional Specifications publicly available today for industry feedback Enhanced deployment flexibility sets stage for new pervasively available core functionality IWOCL – April 27, 2020 – 6:00 AM GMT – Today, The Khronos® Group, an open consortium of industry-leading companies creating advanced interoperability standards, publicly releases the OpenCL™ 3.0 Provisional Specifications. OpenCL 3.0 realigns the OpenCL
This blog post was originally published at Codeplay Software’s website. It is reprinted here with the permission of Codeplay Software. Software developers are looking more than ever at how they can accelerate their applications without having to write optimized processor specific code. SYCL is the industry standard for C++ acceleration, giving developers a platform to
This blog post was originally published at Intel’s website. It is reprinted here with the permission of Intel. As a student pursuing a doctorate in systems design engineering at the University of Waterloo, Alexander Wong didn’t have enough money for the hardware he needed to run his experiments in computer vision. So he invented a
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. This is an updated version of How to Speed Up Deep Learning Inference Using TensorRT. This version starts from a PyTorch model instead of the ONNX model, upgrades the sample application to use… Speeding Up Deep Learning Inference
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. The Virtual Reality (VR) industry is in the midst of a new hardware cycle – higher resolution headsets and better optics being the key focus points for the device manufacturers. Similarly on the software front, there has been
This article was originally published at MathWorks’ website. It is reprinted here with the permission of MathWorks. What Is Object Recognition? Object recognition is a computer vision technique for identifying objects in images or videos. Object recognition is a key output of deep learning and machine learning algorithms. When humans look at a photograph or
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Every year, clever researchers introduce ever more complex and interesting deep learning models to the world. There is of course a big difference between a model that works as a nice demo in… Accelerating WinML and NVIDIA Tensor