Figure 1: An order of magnitude less energy is needed for INT8 (8b Add) operations compared with FP32 operations (32b Add). Image source: AMD Xilinx
The need for machine vision is growing across a range of applications, including security, traffic and city cameras, retail analytics, automated inspection, process control, and vision- guided robotics. Machine vision is complex to implement and requires the integration of diverse technologies and sub-systems, including high-performance hardware and advanced artificial intelligence/machine learning (AI/ML) software. It begins with optimizing the video capture technology and vision I/O to meet the application needs and extends to multiple image processing pipelines for efficient connectivity. It is ultimately dependent on enabling the embedded-vision system to perform vision-based analytics in real time using high- performance hardware such as field programmable gate arrays
(FPGAs), systems on modules (SOMs), systems on chips (SoCs), and even multi-processor systems on chips (MPSoCs) to run the needed AI/ML image processing and recognition software. This can be a complex, costly, and time- consuming process that is exposed to numerous opportunities for cost overruns and schedule delays. Instead of starting from scratch, designers can turn to a well- curated, high-performance development platform that speeds time to market, controls costs, and reduces development risks
while supporting high degrees of application flexibility and performance. A SOM-based development platform can provide an integrated hardware and software environment, enabling developers to focus on application customization and save up to nine months of development time. In addition to the development environment, the same SOM architecture is available in production-optimized configurations for commercial and industrial environments, enhancing application reliability and quality,
further reducing risks, and speeding up time to market.
How to rapidly design and deploy smart machine vision systems
Instead of starting from scratch, designers can turn to a well-curated, high-performance development platform that speeds time, controls costs, and reduces development risks while supporting high degrees of application flexibility and performance.
This article begins by reviewing the challenges associated with the development of high-performance machine vision systems, then presents the comprehensive development environment offered by the Kria KV260 vision AI starter kit from AMD Xilinx , and closes with examples of production-
Written by Jeff Shepard
we get technical
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