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NVIDIA Tops MLPerf AI Inference Benchmarks

Today NVIDIA posted the fastest results on new benchmarks measuring the performance of AI inference workloads in data centers and at the edge — building on the company’s equally strong position in recent benchmarks measuring AI training. “NVIDIA topped all five benchmarks for both data center-focused scenarios (server and offline), with Turing GPUs providing the highest performance per processor among commercially available entries.”

New MLPerf Benchmark Measures Machine Learning Inference Performance

Today a consortium involving over 40 leading companies and university researchers introduced MLPerf Inference v0.5, the first industry standard machine learning benchmark suite for measuring system performance and power efficiency. “Our goal is to create common and relevant metrics to assess new machine learning software frameworks, hardware accelerators, and cloud and edge computing platforms in real-life situations,” said David Kanter, co-chair of the MLPerf inference working group. “The inference benchmarks will establish a level playing field that even the smallest companies can use to compete.”

Qualcomm to bring power-efficient AI Inference to the Cloud

Today Qualcomm announced that it is bringing the Company’s artificial intelligence expertise to the cloud with the Qualcomm Cloud AI 100. “Our all new Qualcomm Cloud AI 100 accelerator will significantly raise the bar for the AI inference processing relative to any combination of CPUs, GPUs, and/or FPGAs used in today’s data centers,” said Keith Kressin, senior vice president, product management, Qualcomm Technologies, Inc.

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.