Simultaneous and Speculative Thread Migration for Improving Energy Efficiency of Heterogeneous Core Architectures

Combined Prediction Energy Model at Software Architecture Level

“…It is applied to two types of processors (intel 486dx2 and Fujitsu sparse 934). Based on equation 1, Bazzaz [10], Tan [11], Lee [12] and Ashouri [13] have proposed fine-grained approach for power consumption analysis and prediction, and also put forward new methods for low power applications.…”

“…For example, at the bottom of the hardware, saving energy are conducted by improving the manufacturing process, optimizing the circuit structure. At instruction level, the instructions optimized by compiler, instruction conversion and rearrangement, and loop structure optimization [9][10][11][12][13] are used for energy management; in the source code layer, code expression change, data representation and program structure rearrangement are optimized, and redundant calculation is eliminated, and data storage space are compressed [14][15][16][17][18][19][20]; at component level, and process level energy management [21][22][23] are used; at architecture layer, macro-modeling, system structure selection, transformation and simplification [24][25][26] are used.…”

“…At present, this work is mainly carried out at several levels. At the hardware level, the power consumption is measured by power meter and power sensor [6][7][8]; at the instruction level, the energy consumption is mainly obtained by statistical method, that is, the energy of software is obtained by accumulating the energy of each instruction or each kind of instructions, instruction pairs, pipeline or cache and other hardware structures during the execution [9][10][11][12][13]; at the source code level, the single line code energy consumption is mainly obtained by linear regression, and then the software energy consumption is obtained by accumulating the energy of each single line code [14][15][16][17][18][19][20]; at the process level, the relationship between the resource utilization rate and energy of the process is established, and then, it accumulates time to obtain process energy consumption [21][22][23]; at the architecture level, the relationship between software high-level features and energy consumption is explored to predict software energy consumption [24][25][26]. Generally, software can be considered as a complex system, and then its architecture can be represented by complex network to study different characteristics of software [28][29][30].…”

Edge Intelligence-Assisted Asymmetrical Network Control and Video Decoding in the Industrial IoT with Speculative Parallelization

“…Nevertheless, when the large data spans multiple NALUs (e.g., in the industry monitoring), this type of NALU sequential analyzing and decoding method could not fully utilize the computing resources, incurring a large processing delay and failing to make a real-time operational decision [18], and thus not adapt to rapidly changing industrial field situations. As such, the simultaneous speculative parallelization has been utilized to speed up the decoding efficiency [19][20][21]. In particular, Lee et al in [21] have proposed simultaneous and speculative backend execution, to offset the performance loss by speeding up the execution in the thread migration, and meanwhile to sustain the low energy overhead.…”

“…As such, the simultaneous speculative parallelization has been utilized to speed up the decoding efficiency [19][20][21]. In particular, Lee et al in [21] have proposed simultaneous and speculative backend execution, to offset the performance loss by speeding up the execution in the thread migration, and meanwhile to sustain the low energy overhead.…”