The growth of sensor technology, communication systems and computation have led to vast quantities of data being available for relevant parties to utilise. Applications such as the monitoring and analysis of industrial equipment, smart surveillance, and fraud detection rely on the 'real-time' analysis of time sensitive data gathered from distributed sources. A variety of processing tasks, such as filtering, aggregation, machine learning algorithms, or other transformations to be carried out on this data in order to extract value from it. Centralised computation strategies are often deployed in these scenarios, with the majority of the data being forwarded though the network to a datacenter environment, typically due to the lack of required computational or storage resources at the leaves of the network, and data from other sources or historical data being required. This approach has also traditionally been viewed as more scalable, as resources can be augmented through the addition of extra compute hardware and cloud services.
|Original language||English (US)|
|Title of host publication||2017 27th International Conference on Field Programmable Logic and Applications, FPL 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Oct 2 2017|