An Accurate Instruction-Level Energy Estimation Model and Tool for Embedded Systems

Bazzaz, Mostafa; Salehi, M T Zahraei; Ejlali, Alireza

doi:10.1109/tim.2013.2248288

Cited by 70 publications

(46 citation statements)

References 10 publications

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“…In order to quantize the execution energy of both codes, the instruction level energy model for ARM7TDMI available in [24]. The instructions used in our codes with their execution energies in nano joules (nJ) are shows in Table 1.…”

Section: Results and Analysismentioning

confidence: 99%

Energy Efficient Frame Structure for Gigabit Passive Optical Networks

Butt

Idrus

Radzi

et al. 2016

IJECE

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Increasing power consumption in information and communication access networks is one of the major cause of greenhouse gas emissions. These emissions are harmful for life on earth. Passive Optical Networks (PONs) are comparatively energy efficient network but the broadcast nature of downstream traffic leads to unnecessary frames processing by the optical network units and cause of energy wastage. Bi-PON technique has solved this problem by changing the XGPON / GPON frame structure into an interleaved pattern. However, this structure also requires additional hardware changings at the optical network units. In this paper, we have tried to achieve the same objective by making a few changings in the GPON frame structure without modifying the existing hardware structure. The simulation results show that 25.25% processing energy of an ONU can be saved by incorporating these changes.

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Section: Results and Analysismentioning

confidence: 99%

Energy Efficient Frame Structure for Gigabit Passive Optical Networks

Butt

Idrus

Radzi

et al. 2016

IJECE

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“…Bazzaz et al [7] modeled the energy consumption of the CPU, Flash memory, SRAM and the memory controller. Since the inter instruction energy cost is about 5% of the base instruction cost, detailed estimation of inter instruction cost is not carried out.…”

Section: Related Workmentioning

confidence: 99%

A Simplified Method for Instruction Level Energy Estimation for Embedded System

Kulkarni¹,

Udupi²

2017

EJERS

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Embedded systems are used extensively in all spheres of life. Size, cost and power are the major issues in design and marketing of these products. In embedded system, the processor has to perform given task repeatedly. Power optimization need to be achieved not only at hardware level, but also at software level. Because software power contributes substantially in overall power consumption of embedded system. In this paper, a simplified method for instruction level energy estimation is presented for ARM Cortex M4 processor. Results obtained are compared with micro benchmark programs. The result shows less than -2% error in energy consumption estimation.

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“…Clearly the proposed online energy management must take into account the time-constraint given by (22). Another important constraint that must be taken into account is for guaranteeing reliability.…”

Section: Online Energy Managementmentioning

confidence: 99%

Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems

Salehi

Ejlali

Al-Hashimi

2016

IEEE Trans. Parallel Distrib. Syst.

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Abstract-This paper proposes an N-modular redundancy (NMR) technique with low energy-overhead for hard real-time multicore systems. NMR is well-suited for multi-core platforms as they provide multiple processing units and low-overhead communication for voting. However, it can impose considerable energy overhead and hence its energy overhead must be controlled, which is the primary consideration of this paper. For this purpose the system operation can be divided into two phases: indispensable phase and on-demand phase. In the indispensable phase only half-plus-one copies for each task are executed. When no fault occurs during this phase, the results must be identical and hence the remaining copies are not required. Otherwise, the remaining copies must be executed in the on-demand phase to perform a complete majority voting. In this paper, for such a two-phase NMR, an energy-management technique is developed where two new concepts have been considered: i) Block-partitioned scheduling that enables parallel task execution during on-demand phase, thereby leaving more slack for energy saving, ii) Pseudo-dynamic slack, that results when a task has no faulty execution during the indispensable phase and hence the time which is reserved for its copies in the on-demand phase is reclaimed for energy saving. The energymanagement technique has an off-line part that manages static and pseudo-dynamic slacks at design time and an online part that mainly manages dynamic slacks at run-time. Experimental results show that the proposed NMR technique provides up to 29% energy saving and is 6 orders of magnitude higher reliable as compared to a recent previous work.

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An Accurate Instruction-Level Energy Estimation Model and Tool for Embedded Systems

Cited by 70 publications

References 10 publications

Energy Efficient Frame Structure for Gigabit Passive Optical Networks

Energy Efficient Frame Structure for Gigabit Passive Optical Networks

A Simplified Method for Instruction Level Energy Estimation for Embedded System

Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems

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