Deep Learning Inference and Visual Inspection @ Intel

Abstract: Recent years have seen significant evolvement of deep learning and AI capabilities. AI solutions can augment or replace mundane tasks, increase workforce productivity and relief human bottlenecks. Unlike traditional automation, these solutions have cognitive aspects which used to require human decision making. In some cases, deep Learning has proven to be even more accurate than humans in identifying patterns and therefore can be effectively used to enable various kinds of automated, real- time decision making.

The advanced Analytics team at Intel has addressed these needs and implemented an internal visual inference platform – a high-performance system for deep learning inference, designed for production environments. This innovative system enables easy deployment of many DL models in production while enabling a closed feedback loop where data flows in and decisions are returned through a fast REST API. The system maximizes throughputs through batching and smart in-memory caching and can be deployed either as a cluster or standalone node.

To enable stream analytics at scale, the system was built in a modern micro-services architecture using cutting edge technologies such as TensorFlow, TensorFlow serving, Redis, Flask and more. It is optimized to be easily deployed with Docker and Kubernetes and to cut down time to market for deploying a DL solution. By supporting different kinds of models and various inputs including images and video streams this system can enable deployment of smart visual inspection solutions with real- time decision making.

The presented platform and analytic capabilities were applied to several use cases at Intel and demonstrated excellent results. For example, in one use case it was able to successfully detect wafer defects based on optic microscope images of each layer. In addition, we found that this capability opens the door for additional high value DL solutions that require large scale inference in production.

Bio: Moty is a principle engineer for big data analytics at Intel IT. He serves as the CTO of the advanced analytics group which delivers Big data and AI solutions across Intel. With over 15 years of experience in analytics, data warehousing, and decision support solutions, Moty runs development and architecture of various big data and AI initiatives such as IoT systems, predictive engines, online inference systems and more. Moty holds a Bachelor’s Degree in Economics and Computer Science and a Master’s Degree in Business Administration from the Ben-Gurion University.

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