Electronic systems’ power consumption has been a real challenge for Hardware and Software designers as well as users especially in portable devices like cell phones and laptop computers.
Measuring real time power dissipation is critical in thermal analysis of a new design of HW like processors (CPU) just as it is important for OS programmers writing process schedulers.
These technologies can be categorized into two main categories: direct measurement using subsystem power sensors and meters or indirect estimation based on provided information such as temperature or performance counters.
The first-order linear model was developed by G. Contreras and M. Martonosi at Princeton University using Intel PXA255 processor to estimate CPU and memory power consumption.
The main challenging issue with this method is computing the power weights using a mathematical model (ordinary Least Squares Estimation) at different voltage/frequency points.
[6] The other challenge is in accessing HPCs; for example, in this case they are being read at the beginning of the main OS timer interrupt which requires a software modification.
[6] In summary, the main benefits of this approach are that it is easy to implement, low cost, and does not require special hardware modification.
This method can not be used by OS scheduler or software developers executing multi threaded programs because it needs to gather data by running benchmarks several times.
(4) Equation 4 transformation can be linear, inverse, logarithmic, exponential, or square root; it depends on what makes the power predication more accurate.
Piece wise linear function was chosen to analyze equation 4 from collected data because it will capture more detail about each processor core power.
As the Integrated Circuit (IC) technology size is getting smaller in nanometer scale and more transistors are put together in that small area, the total power and temperature on chip are also increasing.
[10][11] High chip temperature causes more leakage power consumption, higher interconnect resistance and slower speed of transistors.
The DTM idea is to detect and reduce the temperature of hot units spots in a chip using different techniques like activity migration, local toggling, dynamic voltage and frequency scaling.
[10] A new method was developed by H. Li, P. Liu, Z. Qi, L. Jin, W. Wu, S.X.D Tan, J. Yang at University of California Riverside based on observing the average power consumption of low level modules running typical workload.
Therefore, there is a need for a tool that has the capability to measure power consumption on Smartphones that software designers could use to monitor their applications in real-time.
So the solution to this problem is PowerBooter model that can estimate real-time power consumption for individual Smartphone subsystems such as CPU, LCD, GPS, audio, Wi-Fi and cell phone communication components.
Along with PowerBooter model an on-line PowerTutor utility can use the generated data to determine the subsystem level power consumption.
This is determined by state of discharge (which is total consumed energy by battery) variation within a testing interval captured by voltage sensors that will eventually drive the following equation.
To decrease the effect of internal resistance all the phone components can be switched to their lowest power modes to minimize the discharge current when taking a voltage reading.
Once the model is generated automatically or manually the PowerTutor utility can use the data to estimate power consumption in real time.
Software engineers can use this utility to optimize their design or users can use this tool to make their decision about buying applications based on the power consumption.
For example, software engineer; can observe the power consumption when using different compiling techniques to handle TLB misses and paging.
One can use different types of sensors to gather voltage, current, frequency or temperature and then use those data to estimate power consumption.
The LEAP (Low Power Energy Aware Processing) has been developed by D. McIntire, K. Ho, B. Yip, A. Singh, W. Wu, and W.J.
Kaiser at University of California Los Angeles to make sure the embedded network sensor systems are energy optimized for their applications.
Many modern embedded networked sensors are required to do many things like image processing, statistical high performance computing and communication.
The experimental results shows that the optimal choice of sensor systems, processor, wireless interface, and memory technology is not application dependent but it could be hardware allocation issue.
[19] In conclusion, LEAP differs from previous methods like PowerScope[20] because it provides both real-time power consumption information and a standard application execution environment on the same platform.
One of these methods to capture real time data to validate power or thermal models is an infrared measurement setup developed by F.J. Mesa-Martinez, J.Nayfach-Battilana and J. Renau at University of California Santa Cruz.
The first step is to measure the temperature using IR camera and within the oil coolant that flows over the top of the chip surface, the detailed setup information is described in reference.