This work is taking an innovative approach to CNN [convolutional neural net] architecture optimization that is directly guided by minimization of power consumption using a sophisticated new energy estimation tool, and it demonstrates large performance gains over computation-focused methods.
After studying the minimization of power consumption for a fixed system size, the minimum costs associated with the different system sizes are studied, and the differing trends in brackish-water and high-salinity applications are compared.
The objective functions are formulated for minimization of power consumption in the rolling process, variances between ideal dimensions and design dimensions, as well as variances between the lengths of contact arcs.
Minimization of power consumption in VLSI chips has thus become an important design objective.
Temporal load aware optimization, minimization of power consumption as a function of time has vastly been studied.
The work in [44] formulated the RRH selection problem as a trade-off between the minimization of power consumption and transmission power, while satisfying a series of network constraints (i.e., spectrum limitations and traffic requirements).
The key to providing smart environment based on a mobile device application is in definition of goals and establishing an effective balance of used sensors data providers, where the minimization of power consumption is essential.
In this paper, we present a multi-objective optimization approach for indoor wireless network planning subject to constraints for exposure minimization, coverage maximization and power consumption minimization.
The functional specifications of the VCO optimized in this design are the center frequency and minimization of overall power consumption as well as minimization of power due to gate-oxide tunneling current leakage, a component that was not important in previous generations of CMOS technologies but is dominant at 45 nm and below.
