Artificial Intelligence Now

Chapter 4. Compressed Representations in the Age of Big Data

Ben Lorica

When developing intelligent, real-time applications, one often has access to a data platform that can wade through and unlock patterns in massive data sets. The backend infrastructure for such applications often relies on distributed, fault-tolerant, scaleout technologies designed to handle large data sets. But, there are situations when compressed representations are useful and even necessary. The rise of mobile computing and sensors (IoT) will lead to devices and software that push computation from the cloud toward the edge. In addition, in-memory computation tends to be much faster, and thus, many popular (distributed) systems operate on data sets that can be cached.

To drive home this point, let me highlight two recent examples that illustrate the importance of efficient compressed representations: one from mobile computing, the other from a popular distributed computing framework.

Deep Neural Networks and Intelligent Mobile Applications

In a recent presentation, Song Han, of the Concurrent VLSI Architecture (CVA) group at Stanford University, outlined an initiative to help optimize deep neural networks for mobile devices. Deep learning has produced impressive results across a range of applications in computer vision, speech, and machine translation. Meanwhile the growing popularity of mobile computing platforms means many mobile applications will need to have capabilities in these areas. The challenge ...