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• Stokke, Kristoffer Robin; Stensland, Håkon Kvale; Griwodz, Carsten & Halvorsen, Pål (2017). Load Balancing of Multimedia Workloads for Energy Efficiency on the Tegra K1 Multicore Architecture. I Chen, Kuan-ta; Cesar, Pablo & Hsu, Cheng-Hsin (Red.), Proceedings of the 8th ACM on Multimedia Systems Conference. Association for Computing Machinery (ACM). ISSN 9781450350020. s. 124–135. doi: 10.1145/3083187.3083195. Fulltekst i vitenarkiv
• Stokke, Kristoffer Robin; Stensland, Håkon Kvale; Halvorsen, Pål & Griwodz, Carsten (2016). High-Precision Power Modelling of the Tegra K1 Variable SMP Processor Architecture. I Beux, Sébastien Le (Red.), Proceedings of IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781509035304. s. 193–200. doi: 10.1109/MCSoC.2016.28. Fulltekst i vitenarkiv

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• Stokke, Kristoffer; Stensland, Håkon & Halvorsen, Pål (2016). A High Precision Power Model for the Tegra K1 CPU, GPU and RAM. Fulltekst i vitenarkiv Vis sammendrag

  Power modelling is an important topic in many areas of computing, for example to save energy in texture streaming for gaming[1] or to select efficient H.264 video encoding parameters[2]. However, researchers' view of how hardware consume power is limited. They typically resort to rate­based models to describe the energy consumption of hardware, where power usage is correlated directly with hardware access rates (for example instructions or cache misses per second)[3,4,5,6]. This approach ignores many mechanisms that impact the power usage of a system, such as rail voltages, core­ and clock­ gating, frequency scaling and variable cost of instruction execution. Because of this, they can mispredict up to 70 % on the Tegra K1. We show that by taking all these factors into account with sufficient hardware knowledge, it is possible to bridge the gap between power usage and software execution to build power models which are over 98 % accurate over all CPU, GPU and memory frequency combinations.

• Stokke, Kristoffer Robin; Stensland, Håkon Kvale; Halvorsen, Pål & Griwodz, Carsten (2016). A High Precision Power Model for the Tegra K1 CPU, GPU and RAM". Fulltekst i vitenarkiv
• Stokke, Kristoffer Robin (2016). A High-Precision GPU, CPU and Memory Power Model for the Tegra K1 SoC. Fulltekst i vitenarkiv
• Stokke, Kristoffer Robin; Stensland, Håkon Kvale; Griwodz, Carsten & Halvorsen, Pål (2016). A high-precision, hybrid GPU, CPU and RAM power model for generic multimedia workloads. Fulltekst i vitenarkiv
• Stokke, Kristoffer; Stensland, Håkon; Halvorsen, Pål & Griwodz, Carsten (2015). Why Race-to-Finish is Energy-Inefficient for Continuous Multimedia Workloads. doi: 10.1109/MCSoC.2015.20. Fulltekst i vitenarkiv Vis sammendrag

  It is often believed that a "race-to-finish" approach, where processing is finished quickly, is the best way to conserve energy on modern mobile architectures. However, from earlier work we know that for continuous multimedia workloads, the best way to conserve energy is to minimise processor frequency such that application deadlines are met. In this paper, we investigate the reasons behind this. We develop an original method to model dynamic and static power on individual power rails of the Tegra K1 by only measuring the total power usage of the board. Our model has an average error of only 8 %. We find that the way an application scales performance with frequency is very important for energy efficiency. We demonstrate a 37 % energy saving by minimising processor and memory frequency of a video processing filter such that a framerate of 20 FPS is met.

• Stokke, Kristoffer; Stensland, Håkon; Griwodz, Carsten & Halvorsen, Pål (2015). Energy Efficient Video Encoding Using the Tegra K1 Mobile Processor [Demo Paper]. doi: 10.1145/2713168.2713186. Fulltekst i vitenarkiv Vis sammendrag

  Energy consumption is an important concern for mobile devices, where the evolution in battery storage capacity has not followed the power usage requirements of modern hardware. However, innovative and flexible hardware platforms give developers better means of optimising the energy consumption of their software. For example, the Tegra K1 System-on-Chip (SoC) offers two CPU clusters, GPU offloading, frequency scaling and other mechanisms to control the power and performance of applications. In this demonstration, the scenario is live video encoding, and participants can experiment with power usage and performance using the Tegra K1’s hardware capabilities. A popular power-saving approach is a “race to sleep” strategy where the highest CPU frequency is used while the CPU has work to do, and then the CPU is put to sleep. Our own experiments indicate that an energy reduction of 28 % can be achieved by running the video encoder on the lowest CPU frequency at which the platform achieves an encoding frame rate equal to the minimum frame rate of 25 Frames Per Second (FPS).

• Stokke, Kristoffer; Stensland, Håkon & Halvorsen, Pål (2015). Energy and Performance Optimization of a Simple Video Encoder on the Jetson­-TK1. Fulltekst i vitenarkiv Vis sammendrag

  This poster analyses the energy consumption of a simple video encoder running on NVIDIA's Tegra K1 processor. The total energy consumption of the video encoder is investigated under the influence of different hardware configurations, such as which processors (CPU clusters or GPU) are used, DVFS algorithms, and whether performance optimisations like NEON are implemented. We find that NEON instructions and multithreading generally have positive effects on energy consumption, saving between 25 to 40 % energy compared to a non­optimised, naive implementation. GPU offloading is found to be marginally better than CPU execution by an amount of 1.7 %.

• Stokke, Kristoffer Robin; Stensland, Håkon Kvale; Griwodz, Carsten & Halvorsen, Pål (2015). Energy Efficient Continuous Multimedia Processing Using the Tegra K1 Mobile SoC. Fulltekst i vitenarkiv
• Stokke, Kristoffer Robin; Stensland, Håkon Kvale; Halvorsen, Pål & Griwodz, Carsten (2015). Why Race-to-Finish is Energi-Inefficient for Continuous Multimedia Workloads. Fulltekst i vitenarkiv
• Stokke, Kristoffer Robin; Stensland, Håkon Kvale; Halvorsen, Pål & Griwodz, Carsten (2015). Energy Efficient Video Encoding Using the Tegra K1 Mobile Processor. Fulltekst i vitenarkiv
• Stokke, Kristoffer Robin; Stensland, Håkon Kvale & Halvorsen, Pål (2015). Energy and Performance Optimisation of a Simple Video Encoder on the Jetson­TK1. Fulltekst i vitenarkiv

Se alle arbeider i NVA