The goal of this chapter is to discuss how software can be used to optimize power consumption, starting with the basics of what power consumption consists of, how to properly measure power consumption, and then moving on to techniques for minimizing power consumption in software at the algorithmic level, hardware level, and data-flow level.
The roles, responsibilities and reporting timelines were clearly defined on the surveillance data flow for each level to avoid confusion and to ensure regular submission of data.
Section 2 follows the results obtained from characterizing pyrosequencing data at the flow level, and in Section 3, we present the Flowsim simulator and some results obtained from comparing simulated and real data sets.
However, it does pose issues regarding processing efficiency, rigid data flow, and low-level abstraction.
Hence, we kept the processing cost, with this massive data flow, to the minimal level.
Finally the Acquire Recognise Cluster (ARC) SOA for reconfigurable manufacturing process monitoring systems is reviewed, to provide a topological view of data flow within a field level manufacturing SOA.
The bottom-up data flow proceeds from a preprocessing level to the hypothesis level where object hypotheses created by exhaustive object detection algorithms are represented in a roughly retinotopic way.
Fig. 2. Data flow in a typical Level-2 processing chain.
Pig [20]: A high-level data flow language and execution framework for parallel computation.
To achieve the best performance, a cloud-based architecture is proposed with on-demand dynamic allocation of resources and a multi-level data flow with caching capabilities.
Although some of the models may seem to perform a small number of operations, we argue that the main nature of Simulink-like models is the implementation of data flow systems in different levels of abstraction.
