Xilinx Reconfigurable Acceleration Stack Delivers Fastest Path to 2-6x Compute Efficiency over FPGA Competition

Today Xilinx FPGAs enable hyperscale data centers to achieve 2-6x the compute efficiency in machine learning inference, derived from DSP architectural advantages for limited precision data types, superior on-chip memory resources, and greater than one year technology lead over FPGA competition. 

The Xilinx Reconfigurable Acceleration Stack includes math libraries designed for cloud computing workloads, application libraries integrated with major frameworks, such as Caffe for machine learning, a PCIe®-based development board and reference design for high density servers, and an OpenStack support package making Xilinx FPGA-based accelerators easy to provision and manage.

"Flexibility and feature velocity – the ability to add new features rapidly through software – are important for hyperscale's complex and constantly changing application landscape," said Karl Freund, senior analyst, Machine Learning, for Moor Insights & Strategy. "The capability to reconfigure and optimize the accelerator as algorithms evolve – as Xilinx claims – is a compelling advantage in hyperscale environments."

"The new stack accelerates mainstream adoption of our FPGAs in hyperscale data centers," said Nazeem Noordeen, corporate vice president, IP Solutions at Xilinx. "It delivers the fastest path to realize 2-6x the compute efficiency over FPGA competition."

Availability
The Xilinx Reconfigurable Acceleration Stack is available to all major cloud service providers today.  To learn more visit the Xilinx Acceleration Zone at www.xilinx.com/accelerationstack. 

About Xilinx
Xilinx is the leading provider of All Programmable FPGAs, SoCs, MPSoCs, and 3D ICs. Xilinx uniquely enables applications that are both software defined and hardware optimized – powering industry advancements in Cloud Computing, Embedded Vision, Industrial IoT, and 5G Wireless. For more information, visit www.xilinx.com.

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Xilinx
Silvia E. Gianelli
(408) 626-4328
[email protected] 

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SOURCE Xilinx, Inc.

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