In this video from SC16, Dr. Eng Lim Goh from HPE/SGI discusses new trends in HPC Energy Efficiency and Deep Learning.
From customer behavior and supply chains, to financial and employee conditions, to social media and security threats, business and government agencies produce and capture ever-increasing amounts of data. Unlocking value within these data assets is the purpose of analytics. SGI High Performance Solutions for Data Analytics help you unlock value at the speed of memory, with near limitless scale and mission-critical reliability using SAP HANA, Oracle Database In-Memory, and Apache Hadoop. SGI’s leadership in data analytics derives from deep expertise in High Performance Computing and over two decades delivering many of the world’s fastest supercomputers. Leveraging this experience and SGI’s innovative shared and distributed memory computing solutions for data analytics enables organizations to achieve greater insight, accelerate innovation, and gain competitive advantage.
For the purposes of this discussion, “Deep Learning” is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations are better than others at simplifying the learning task (e.g., face recognition or facial expression recognition). One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.