Hardware designs need to obey constraints of resource utilization, minimum clock frequency, power consumption, computation precision and data range, which are all affected by the data type representation.
Furthermore, platform power consumptions, computation energy efficiency, and platform cost efficiency of the experimental heterogeneous embedded platforms were also evaluated, which demonstrated that the investigated heterogeneous embedded computing platforms were suitable for real-time portable or mobile high-quality medical ultrasonic imaging device constructions.
In this paper, as the first step towards reasoning about resource/energy consumption of computations, we assume that computations only require CPU resources.
To name a few: Unbalanced energy consumption: since computations and communications are concentrated in one and a few nodes, respectively.
However, meeting QoS requirements in WSNs introduces certain overhead to routing protocols in terms of energy consumption, intensive computations, and significantly large storage.
An originality of our method is that matching information of already computed patterns is materialized as bitmaps, which greatly reduces both memory consumption and computation costs.
The algorithm is combined with parallel processing, modern general purpose Compute Unified Device Architecture (CUDA) Graphic Processing Unit (GPU) to reduce significantly the time consumption of computation of using traditional Central Processing Unit (CPU).
Then, we conclude a discussion of energy consumption on computation.
It enabled to perform the theoretical study with a reasonable consumption of computation time.
In some application of mobile autonomous robots, the energy consumption and computation ability are constrained.
