System-level power & energy estimation methodology and optimization techniques for CPU-GPU based mobile platforms
Tipo de documentoTexto en actas de congreso
Fecha de publicación2015
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condiciones de accesoAcceso restringido por política de la editorial
Due to the growing computational requirements of mobile applications, using a heterogeneous Multiprocessor System-on-Chip becomes an incontrovertible solution to meet the service requirements. Today, Electronic System-Level design is considered as a vital premise to explore design trade-offs for such devices in the early stage of the design flow. This paper proposes a novel system-level power/energy estimation methodology and optimization techniques for heterogeneous CPU-GPU based platforms. There are two parts involved in this methodology. First, we developed the power models by using functional parameters to set up generic power models for different parts of the platform. Second, we designed a simulation based system-level prototype using SystemC (JIT) and Cycle-Accurate simulators to accurately evaluate the activities used in the related power models. The combination of the two parts leads to a novel power estimation methodology at system-level, which gives a good trade-off between accuracy and speed. Moreover, leveraging our methodology, we introduce novel power optimization techniques such as inter-task DVFS and workload balancing at the system-level for CPU-GPU platforms. The efficiency of our proposed methodology and optimization techniques are validated through a CARMA kit, which consists of an ARM quad-core processor and a NVIDIA GPU processor (96 cores). Estimated power and energy values are compared to real board measurements. Our obtained power/energy estimation results provide less than 2.5% of error for single core processor, 4% for dual-core processor, 4% for quad-core, 4% for GPU and 6% multi-processor based systems. By using the proposed optimization techniques, we achieved significant power and energy savings of up to 45% and 70% respectively for various industrial benchmarks.
CitaciónRethinagiri, S. [et al.]. System-level power & energy estimation methodology and optimization techniques for CPU-GPU based mobile platforms. A: IEEE Symposium on Embedded Systems for Real-Time Multimedia. "2014 IEEE 12th Symposium on Embedded Systems for Real-time Multimedia (ESTIMedia 2014): New Delhi, India: 16-17 October 2014". Greater Noida: Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 118-127.
|System-level po ... based mobile platforms.pdf||System-level power & energy estimation methodology and optimization techniques for CPU-GPU based mobile platforms.pdf||1.848Mb||Acceso restringido|