Artificial Intelligence with New Intel® Xeon® Scalable Processors: Most Agile AI Platform
The New Intel® Xeon® processor Scalable family provides scalable performance for widest variety of AI & other datacenter workloads – including deep learning. The New Intel® Xeon® Scalable processor platform offers built-in Return on Investment (ROI), potent performance and production ready support for AI deployments.
In our smart and connected world, machines are increasingly learning to sense, reason, act, and adapt in the real world. Artificial Intelligence (AI) is the next big wave of computing, and Intel uniquely has the experience to fuel the AI computing era. AI will let us accelerate solutions to large-scale problems that would otherwise take months, years, or decades to resolve.
AI will unleash new scientific discoveries, automate undesirable tasks and extend our human senses and capabilities. Today, machine learning (ML) and deep learning (DL) are two underlying approaches to AI, as are reasoning-based systems.
Deep learning is the most rapidly emerging branch of machine learning, in many cases supplanting classic ML, relying on massive labeled data sets to iteratively “train” many-layered neural networks inspired by the human brain. Trained neural networks are used to “infer” the meaning of new data, with increased speed and accuracy for processes like image search, speech recognition, natural language processing, and other complex tasks.
Architectural improvements together with enhanced software optimizations demonstrates potent performance on the New Intel® Xeon® server-class platforms upto 138X improvement in inference and 113X improvement in training compared to older systems running unoptimized software. (Refer to config details below).
Here we present some AI workloads showing advanced inference and training performance with the New Intel® Xeon® Scalable processors compared to the previous generation Intel® Xeon® processors. All performance measurements are accurate as of July 11th 2017.1
Inference Throughput
Inference is performed at the data center or at the edge. Key metrics for inference performance is throughput and Total Cost of Ownership (TCO). Inference is unsupervised and the output classification can be fed into a number of different usages including – a dashboard for visualization or a decision tree for automatic decision making.
Here we show inference throughput for image recognition workloads using multiple frameworks such as TensorFlow, Caffe, Neon and MxNet and multiple topologies such as AlexNet, GoogleNet V1, ResNet 50, VGG-19.
Training Throughput
Today training runs in the data center. The key metrics are time to train and Total Cost of Ownership (TCO). Training typically requires a large labeled dataset. A portion of this dataset is used to iteratively adjust the connection weights in the neural network. Another portion of this dataset is used to test error rate of the trained neural network, in order to ensure that the data wasn’t overfitted.
Here we show training throughput for image recognition workloads using multiple frameworks such as TensorFlow, Caffe, Neon and MxNet and multiple topologies such as AlexNet, GoogleNet V1, ResNet 50, VGG-19.
Time to Train
Begin your AI journey today using existing, familiar Intel® Xeon® infrastructure with server-class reliability and increase datacenter utilization without additional, unique investments. Here we show time to train performance (lower is better) for image recognition workload using Caffe framework and GoogleNet V1 topology.
Informações de produto e desempenho
Os resultados de benchmark foram obtidos antes da implementação de patches de software e atualizações de firmware destinadas a solucionar violações referentes a "Spectre" e "Meltdown". A implementação dessas atualizações pode tornar estes resultados inaplicáveis para seu dispositivo ou sistema.
Talvez o software e as cargas de trabalho utilizados nos testes de desempenho tenham sido otimizados apenas para desempenho em microprocessadores Intel®. Testes de desempenho, como SYSmark* e MobileMark*, são medidos usando sistemas de computação, componentes, software, operações e funções específicos. Qualquer modificação em algum desses fatores pode provocar variação nos resultados. Consulte outras informações e outros testes de desempenho para ajudá-lo a avaliar melhor as suas compras, incluindo o desempenho desse produto quando combinado com outros produtos. Para obter informações mais completas, visite http://www.intel.com.br/benchmarks.