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FPGAs and the Road to Reprogrammable HPC

In this special guest feature from Scientific Computing World, Robert Roe writes that FPGAs provide an early insight into possibile architectural specialization options for HPC and machine learning. “Architectural specialization is one option to continue to improve performance beyond the limits imposed by the slow down in Moore’s Law. Using application-specific hardware to accelerate an application or part of one, allows the use of hardware that can be much more efficient, both in terms of power usage and performance.”

Xilinx to Acquire Solarflare

Today Xilinx announced today that it has entered into a definitive agreement to acquire Solarflare Communications. “The acquisition will enable Xilinx to combine its industry-leading FPGA, MPSoC and ACAP solutions with Solarflare’s ultra-low latency network interface card (NIC) technology and Onload application acceleration software, to enable new converged SmartNIC solutions, accelerating Xilinx’s “data center first” strategy and transition to a platform company.”

Xilinx Steps Up with Alveo FPGA boards and Versal Adaptive Compute Acceleration Platform

Today FPGA maker Xilinx unveiled Versal, “the industry’s first adaptive compute acceleration platform (ACAP)”. The company also announced new Alveo FPGA cards, which the company claims can deliver “4X the performance of GPUs, 90X the performance of CPUs, plus unprecedented adaptability across workloads.” AMD, one of the Xilinx partners that is showcasing products based on the new Alveo boards, announced a server that will set a new world record for real-time AI inference processing, with a mind-boggling 30,000-images-per-second inference throughput.

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.