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Xilinx introduces Kria Portfolio of Adaptive System-on-Modules for AI Edge innovation
Photo Credit To Xilinx

Xilinx introduces Kria Portfolio of Adaptive System-on-Modules for AI Edge innovation

Xilinx introduces Kria Portfolio of Adaptive System-on-Modules for AI Edge innovation

Xilinx, Inc. today introduced the Kria™ portfolio of adaptive system-on-modules (SOMs), production-ready small form factor embedded boards that enable rapid deployment in edge-based applications.

Coupled with a complete software stack and pre-built, production-grade accelerated applications, Kria adaptive SOMs are a new method of bringing adaptive computing to AI and software developers.

The first product available in the Kria SOM portfolio, the Kria K26 SOM, specifically targets vision AI applications in smart cities and smart factories. The Xilinx® SOM roadmap includes a full range of products, from cost-optimized SOMs for size and cost-constrained applications to higher performance modules that will offer developers more real-time compute capability per watt.

“Xilinx’s entrance into the burgeoning SOM market builds on our evolution beyond the chip-level business that began with our Alveo boards for the data centre and continues with the introduction of complete board-level solutions for embedded systems,” said Kirk Saban, vice president, Product and Platform Marketing at Xilinx. “The Kria SOM portfolio expands our market reach into more edge applications and will make the power of adaptable hardware accessible to millions of software and AI developers.”

According to industry reports, the SOM market is growing at roughly 11 percent annually and total market revenue is expected to reach $2.3 billion by 2025.

 

Xilinx introduces Kria Portfolio of Adaptive System-on-Modules for AI Edge innovation

Faster Time to Deployment

Kria SOMs harness the power, performance and flexibility advantages of Xilinx adaptable hardware, delivered as production-deployable, adaptive modules. Kria SOMs enable rapid deployment by providing an end-to-end board-level solution with a pre-built software stack. By allowing developers to start at a more evolved point in the design cycle compared to chip-down design, Kria SOMs can reduce time-to-deployment by up to nine months.

The Kria K26 SOM is built on top of the Zynq® UltraScale+™ MPSoC architecture, which features a quad-core Arm® Cortex™-A53 processor, more than 250 thousand logic cells, and a H.264/265 video codec. The SOM also features 4GB of DDR4 memory and 245 IOs, which allow it to adapt to virtually any sensor or interface. With 1.4 tera-ops of AI compute, the Kria K26 SOM enables developers to create vision AI applications offering more than 3X higher performance at lower latency and power compared to GPU-based SOMs, critical for smart vision applications like security, traffic and city cameras, retail analytics, machine vision, and vision guided robotics.

Enabling Millions of Software Developers

Xilinx has invested heavily in its tool flows to make adaptive computing more accessible to AI and software developers without hardware expertise. The Kria SOM portfolio takes this accessibility to the next level by coupling the hardware and software platform with production-ready vision accelerated applications. These turnkey applications eliminate all the FPGA hardware design work and only require software developers to integrate their custom AI models, application code, and optionally modify the vision pipeline – using familiar design environments, such as TensorFlow, Pytorch or Café frameworks, as well as C, C++, OpenCL™, and Python programming languages—enabled by the Vitis™ unified software development platform and libraries.

With this new accelerated-application paradigm for software-based design, Xilinx is announcing the first embedded app store for edge applications. Building out beyond its Alveo™ catalogue of apps for data centre, the Xilinx App Store now offers customers a wide selection of apps for Kria SOMs from Xilinx and its ecosystem partners. Xilinx offerings are open source accelerated applications, provided at no-charge, and range from smart camera tracking and face detection to natural language processing with smart vision.

Kria SOMs also enable customization and optimization for embedded developers with support for standard Yocto-based PetaLinux and, for the first time ever, Xilinx is announcing a coming collaboration with Canonical to provide Ubuntu Linux support, the highly popular Linux distribution used by AI developers. This offers widespread familiarity with AI developers and interoperability with existing applications. Customers can develop in either environment and take either approach to production. Both environments will come pre-built with a software infrastructure and helpful utilities.

“For smart vision applications, developers and innovators want the Ubuntu experience they’re used to from cloud to desktop,” said Thibaut Rouffineau, vice president of marketing, Canonical/Ubuntu. “Together with Xilinx, we’re excited to provide Kria SOM customers with out-of-the-box productivity, frictionless transition from development to production, and guaranteed stability and security in the field.”

Xilinx introduces Kria Portfolio of Adaptive System-on-Modules for AI Edge innovation

Out-of-the-Box Ready, Low-cost Development Kit

The Kria KV260 Vision AI Starter Kit provides an affordable and easy-to-use development platform for designing vision applications, right out of the box. The kit is purpose-built to support accelerated vision applications available in the Xilinx App Store and is an excellent way to get up and running in less than an hour with no knowledge of FPGAs or FPGA tools.

Part of the Kria development experience is a self-enabled path for exploration, design, and ultimately production deployment through a vast set of online resource. Hobbyists, makers, and commercial developers alike can accelerate through each phase of the design cycle with tutorial videos, training courses, and a vast ecosystem of providers offering accelerated applications, design services, and more.

Post source : Xilinx

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