Towards an Integer Approximation of Undirected Graphical Models

Data analytics for streaming sensor data brings challenges for the resource efficiency of algorithms in terms of execution time and the energy consumption simultaneously. Fortunately, optimizations which reduce the number of CPU cycles also reduce energy consumption. When reviewing the specifications of processing units, one finds that in-teger arithmetic is usually cheaper in terms of instruction latency, i.e. it needs a small number of clock cycles until the result of an arithmetic instruction is ready. This mo-tivates the reduction of CPU cycles in which code is executed when designing a new, resource-aware learning algorithm. Beside clock cycle reduction, limited memory usage is also an important factor for small devices. Outsourcing parts of data analysis from data centers to ubiquitous devices that actu-ally measure data would reduce the communication costs and thus energy consumption. If, for instance, a mobile medical device or smartphone can build a probabilistic model of the usage behavior of its user, energy models can be made more accurate and power management can be more efficient. The biggest hurdle in doing this, are the heavily restricted computational capabilities of very small devices—some do not even have