Limits of instruction-level parallelism
ASPLOS IV Proceedings of the fourth international conference on Architectural support for programming languages and operating systems
Power analysis of embedded software: a first step towards software power minimization
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low-power design
Validation of an architectural level power analysis technique
DAC '98 Proceedings of the 35th annual Design Automation Conference
Architecture-level power estimation and design experiments
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Run-time power estimation in high performance microprocessors
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Heterogeneous Chip Multiprocessors
Computer
MiBench: A free, commercially representative embedded benchmark suite
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
A systematic method for functional unit power estimation in microprocessors
Proceedings of the 43rd annual Design Automation Conference
Amdahl's Law in the Multicore Era
Computer
ParMiBench - An Open-Source Benchmark for Embedded Multiprocessor Systems
IEEE Computer Architecture Letters
Communications of the ACM
Dark silicon and the end of multicore scaling
Proceedings of the 38th annual international symposium on Computer architecture
On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing
SAAHPC '11 Proceedings of the 2011 Symposium on Application Accelerators in High-Performance Computing
OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems
Computing in Science and Engineering
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Most mobile computing devices use a battery as their main power source. Efficient power use and management are very important in mobile devices. It is necessary to understand the power consumption of the processor in a mobile device. In this paper, we choose to use a PMU (Performance Monitoring Unit) to understand hardware performance. By using a PMU counter, it is possible to analyze mobile AP (Application Processor) quickly and accurately without additional CPU overhead. We built a power consumption model using the number of memory accesses per cycle and the clock frequency of the core of mobile AP. By using the power consumption model that we built, we can easily and quickly calculate the power consumption of each core in a multi-core mobile AP. We measured the power consumption of mobile AP using the Mibench benchmark suite and then compared actual measured power consumption and predicted power consumption using several benchmark applications of the Parmibench benchmark suite. The average RMSE between actual measured power consumption and predicted power consumption was 5.01%.