Edge applications such as advanced driver-assistance systems (ADAS) and autonomous driving (AD) in next-generation cars are fueling the need for large amounts of sensor data from image, radar, and Lidar sensors to be captured and processed to make intelligent decisions in real time. AD platforms are still in their infancy with evolving architectures. These platforms are expected to have many different configurations (number of sensors, resolutions, and types of sensors) needing very flexible yet optimal architectures for edge use cases. Xilinx’s Versal® AI Core ACAP with extensible I/O flexibility supports many sensor interfaces and varied configurations, with a programmable network on chip (NoC) and scalable memory subsystems that enable efficient data movement around key functional blocks. AI Engines and Adaptable Engines accelerate convolutional neural network (CNN) processing overlays, and image sensor processing functions provide a flexible yet optimal solution for next-generation automotive needs.

Check out the winners of the 2021 Adaptive Computing Challenge Developer Contest.

Learn about Vitis™ AI 1.4, the powerful machine learning development platform for AI inference acceleration on Xilinx Adaptive Computing platforms.

Xilinx announces the results of its MLPerf benchmark results for the Alveo U250 accelerator card.

Learn how CERN is leveraging Xilinx Inference AI Acceleration to power scientific breakthroughs.

AMD Composable Kernel library: create highly efficient fused kernels for AI applications with just a few lines of code.

Learn how AMD is addressing the challenge of delivering high performance backend kernels for a wide range of fused tensor operators at rapid pace with the AMD Composable Kernel (CK) library. Chao Liu, Jing Zhang [1]

In 2021, AMD announced a new vision - a 30x25 goal - to achieve 30x energy efficiency improvement by 2025 from a 2020 baseline for our accelerated data center compute nodes. Built with AMD EPYC™ CPUs and AMD Instinct™ accelerators, these nodes are designed for some of the world’s fastest-growing computing needs in AI training and HPC Applications. We plan to share updates annually on where we stand, and this is our first progress report on 30x25.

Today, November 8, 2021, marks the beginning of a new era in GPU-accelerated computing with the announcement of the latest AMD Instinct™ MI200 series accelerators, featuring the world’s fastest – the Instinct MI250X1. The GPU technology being used to power the Frontier supercomputer, which is expected to be the first US exascale system —at 1.5 exaFLOPs— will be available for leading HPC and AI practitioners to use for new industry and research discoveries.

GPU accelerated machine learning training is now broadly available to a broad range of students and professionals across all DirectX 12-capable GPUs from AMD with the release of Windows 11.

To solve the world’s most profound challenges, you need powerful and accessible machine learning (ML) tools that are designed to work across a broad spectrum of hardware. This can range from datacenter applications for scientists and researchers to desktop and notebook PCs used by students and professionals looking to develop ML models on the hardware they already own.

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