Based around the Xilinx Versal AI Core series, the ADM-PA100 offers fully customizable IO and meets requirements for a range of markets including data center, machine learning, HPC, scientific instrumentation, and test and measurement. The Versal AI Core series includes an array of Xilinx AI engines (dedicated VLIW processors, capable of vector math processing at compute densities 5x higher than programmable logic), closely coupled with programmable logic allowing highly efficient implementation of custom coprocessing operations in this data flow. The Xilinx Versal series of devices also feature an on-chip programmable network on chip (NoC) that improves on-chip programmable logic routing in large designs), dedicated hardened IP for multi-rate 100G Ethernet, hardened PCIe Gen4 endpoints with DMA outside the programmable logic, hardened DDR4 memory controllers, built in ARM A72 and R5F CPUs, and programmable logic and DSP performance a generation on from UltraScale devices, says the company. Manuel Uhm, director of Silicon Marketing at Xilinx says, "The hardware adaptability and heterogeneous architecture of Versal AI Core ACAPs are a key advantage over traditional accelerators that typically focus on a subset of applications. This enables the creation of multiple domain specific architectures targeted to specific workloads. We're delighted that Alpha Data has chosen Versal AI Core series for its ADM-PA100 board to accelerate a breadth of workloads in cloud, networking, and edge markets."

In the past two years, artificial intelligence has morphed from academic marvel to global megatrend. Machine learning in some form is set to revolutionize almost everything -- consumer, automotive, industrial, every area of electronics -- and, beyond that, to affect society and our lives in ways we don't yet know about. What this means for the industry is that practically every processor vendor has identified machine learning as a goose that will lay golden eggs. The race is on to position one's own approach as the right solution to accelerate specific workloads in the area that holds the most potential: machine learning outside the data center, or AI at the edge. AI at the edge holds so much promise because it can be applied to practically every electronic device, from self-driving vehicles that see pedestrians in the road to coffee makers that respond to voice commands.