When an instance of a mOSAIC component runs in a Cloud environment, it consumes hardware or software resources, e.g. state-full resources hosted by Cloud service provider and accessible through dedicated APIs.
In SCO (sampling clock offset) compensation, continuously updating the filter coefficients in real time may consume more hardware resources and even more when the number of taps required are increased [9, 10].
Moreover, we point out that equiripple minimum phase digital filters consume more hardware resources than Chebyshev type II digital filters, although the first one deal better with the field-programmable gate array constraints when more bits are used to implement the digital filter.
The EC of an operation is the total power consumed by hardware resources.
Compared with the works in [13, 16], due to the different targeted video specification, our design consumes more hardware cost.
Compared to traditional techniques, fault self-repair based on EHW consumes fewer hardware resources, and the scope of fault self-repair was expanded significantly.
The experiment results show that the architecture consumes less hardware and power resources while retaining a high-level of detection capability in processing 1024 × 1024 images.
By taking into account the energy consumed by hardware and auxiliary equipment, we address key shortcomings of most existing approaches to the challenge of boosting energy efficiency of cellular networks.
In doing so, we balance different forms of energy consumption in an optimal manner by taking into account both the load-dependent energy used for transmission and the static energy consumed by hardware regardless of the actual load.
However, in order to utilize the capacity surpluses for significant energy savings, it is essential to reduce the energy consumed by hardware and auxiliary equipment (e.g., coolers), which is a dominant form of energy consumption in current mobile networks.
