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Xilinx Acquires DeePhi Tech, a Machine Learning Startup based in China

Today FPGA maker Xilinx announced that it has acquired DeePhi Technology, a Beijing-based privately held start-up with industry-leading capabilities in machine learning, specializing in deep compression, pruning, and system-level optimization for neural networks. “Xilinx will continue to invest in DeePhi Tech to advance our shared goal of deploying accelerated machine learning applications in the cloud as well as at the edge.”

Unified Deep Learning Configurations and Emerging Applications

This is the final post in a five-part series from a report exploring the potential machine and a variety of computational approaches, including CPU, GPU and FGPA technologies. This article explores unified deep learning configurations and emerging applications. 

Nimbix to Host the 2018 HPC Cloud Summit on June 6 in Silicon Valley

Today HPC Cloud provider Nimbix announced that their 2018 HPC Cloud Summit will take place June 6 in Silicon Valley. “We are bringing together the best and brightest minds in accelerated computing at the Computer History Museum, an institution dedicated to the preservation and celebration of computer history. Event sponsors include: Intel, Lenovo and Mellanox.”

DNN Implementation, Optimization, and Challenges

This is the third in a five-part series that explores the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores DNN implementation, optimization and challenges. 

Exploring the Possibilities of Deep Learning Software

This is the second post in a five-part series from a report that explores the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores the possibilities and functions of software for deep learning.

The Machine Learning Potential of a Combined Tech Approach

This is the first in a five-part series from a report exploring the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores the machine learning potential of taking a combined approach to these technologies. 

Unified Deep Learning with CPU, GPU and FPGA Technologies

Deep learning and complex machine learning has quickly become one of the most important computationally intensive applications for a wide variety of fields. Download the new paper — from Advanced Micro Devices Inc. (AMD) and Xilinx Inc. — that explores the challenges of deep learning training and inference, and discusses the benefits of a comprehensive approach for combining CPU, GPU, FPGA technologies, along with the appropriate software frameworks in a unified deep learning architecture.

Xilinx Demonstrates Breakthrough Optical Networking on the road to 7 nm

Today Xilinx announced the FPGA industry’s first demonstration of breakthrough 112G PAM4 electrical signaling technology for optical networks, as well as announcing the addition of 58G PAM4 transceivers to its 16nm Virtex UltraScale+ portfolio. “Xilinx has a long history of driving standards efforts and pushing performance limits in serial interconnect technology, and continues to do so with the industry’s first 112G PAM4 demo as well as with its 58G PAM4 solution, here today for customers to begin designing in with,” said Karl Freund, senior analyst, HPC and machine learning, Moor Insights & Strategy. “Today’s Xilinx announcements represent a significant leap forward for network architects who continue to be challenged to improve bandwidth performance of their optical networks.”

Mellanox Announces Innova-2 FPGA-Based Programmable Adapters

Today Mellanox announced the Innova-2 product family of FPGA-based smart network adapters. Innova-2 is the industry leading programmable adapter designed for a wide range of applications, including security, cloud, Big Data, deep learning, NFV and high performance computing. “Xilinx is pleased that our All Programmable UltraScale FPGAs are accelerating Mellanox’s Innova network adaptors,” said Manish Muthal, vice president of Data Center Business at Xilinx. “Our combined technology enables the rapid deployment of customized acceleration for emerging data center and high performance computing workloads.”

“The Ultimate Trading Machine” from Penguin Computing sets Record for Low Latency

The world of High Frequency Trading is all about reducing latency to make money. At the recent STAC Summit in Chicago, a Penguin Computing device called The Ultimate Trading Machine achieved a record-low 98 nanosecond tick-to-trade latency, some 18% faster than the previous world record. “Facing tremendous pressures to optimize the transaction lifecycle, the financial services industry helps drive innovations in many core technologies. At Penguin Computing, we empower our customers with with open technology solutions that achieve performance requirements while keeping costs low and avoiding vendor lock-in. We are proud to join with our partners to deliver this Ultimate Trading Machine.”