Optimizing Deep Learning Performance

                  Optimizing Deep Learning Performance

                  Optimizing Deep Learning Performance

                  Deep learning and other forms of artificial intelligence(AI) offer exciting potential to streamline medical imaging workflows, enhance image quality, and increase the research value of imaging data. To deliver on the promise of AI-enhanced medical imaging, developers and other innovators must deploy their AI solutions on flexible platforms that provide high performance and scalability for deep ...learning innovations without driving up costs. As part of its company-wide commitment to the AI revolution, Intel offers processors, tools, and frameworks for high-performance deep learning on cost-effective, general-purpose Intel? architecture. In addition to Intel? Xeon? Scalable processors and Intel? Solid State Drives, key technologies include Intel’s Deep Learning Deployment Toolkit and the Intel? Math Kernel Library for Deep Neural Networks (Intel? MKL-DNN). These technologies provide an easy way for innovators to deploy and integrate their deep learning models in optimal fashion on various Intel architectures from a variety of frameworks and training platforms. Using Intel? architecture, developers can deliver their innovations without adding costs and complexity to the deployment environment. Intel and GE Healthcare explored the Intel? technologies with one of GE’s deep learning image-classification solutions. They found that optimizing the trained GE solution with Intel’s Deep Learning Deployment Toolkit and Intel? MKL-DNN improved throughput an average of 14 times over a baseline version of the solution and exceeded GE’s throughput goals by almost six times. These findings show a path to practical AI deployment for next-generation diagnostic scanners and a new era of smarter medical imaging.

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