Abstract
An enormous amount of data is constantly being produced around the world, both in the form of large volume as in that of large velocity. Turning the data into information requires many steps of data analysis: methods for filtering and cleaning the data, joining heterogeneous sources, extracting and selecting features, summarizing and aggregating the data, learning predictions, estimating the uncertainties of the learned model and monitoring the model fitness in its deployment. All these processes need to scale up, whether the long analysis workflows are integrated into a deep learning architecture, or not. The data ecosystems no longer allow us to take von Neumann architecture for granted where only compilers or application systems address hardware issues. Specialized architectures for accelerating machine learning have been developed, and machine learning algorithms have been tailored to novel computer architectures. Both trends are aiming at efficiency, in particular the efficient use of given resources: the real time of execution, the amount of energy, memory and communication. In the struggle for sustainability resource restrictions are of utmost importance. Energy consumption in particular receives considerable attention.We believe that resource efficiency cannot be achieved by better machine learning algorithms or by better hardware architectures alone. It demands the smart combination of hardware and algorithms.
