Energy-Aware Compilation and Hardware Design for VLIW Embedded Systems

Tomorrow's embedded devices need to run multimedia applications demanding high computational power with low energy consumption constraints. In this context, the register file is a key source of power consumption and its inappropriate design and management severely affects system power. In this paper, we present a new approach to reduce the energy of shared register files in forthcoming embedded VLIW processors running real-life applications up to 60% without performance penalty. This approach relies on limited hardware extensions and a compiler-based energy-aware register assignment algorithm to deactivate at run-time parts of the register file (i.e., sub-banks) in an independent way

A Survey of FPGA Optimization Methods for Data Center Energy Efficiency

This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGA energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the European Code of Conduct for Data Center Energy Efficiency. The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector.Comment: Accepted for publication in IEEE Transactions on Sustainable Computin

Hierarchical Agent-based Adaptation for Self-Aware Embedded Computing Systems

Siirretty Doriast

Optimizing energy efficiency of CNN-based object detection with dynamic voltage and frequency scaling

On the one hand, accelerating convolution neural networks (CNNs) on FPGAs requires ever increasing high energy efficiency in the edge computing paradigm. On the other hand, unlike normal digital algorithms, CNNs maintain their high robustness even with limited timing errors. By taking advantage of this unique feature, we propose to use dynamic voltage and frequency scaling (DVFS) to further optimize the energy efficiency for CNNs. First, we have developed a DVFS framework on FPGAs. Second, we apply the DVFS to SkyNet, a state-of-the-art neural network targeting on object detection. Third, we analyze the impact of DVFS on CNNs in terms of performance, power, energy efficiency and accuracy. Compared to the state-of-the-art, experimental results show that we have achieved 38% improvement in energy efficiency without any loss in accuracy. Results also show that we can achieve 47% improvement in energy efficiency if we allow 0.11% relaxation in accuracy

Compiler-Driven Leakage Energy Reduction in Banked Register Files

Tomorrow’s embedded devices need to run high-resolution multimedia applications which need an enormous computational complexity with a very low energy consumption constraint. In this context, the register file is one of the key sources of power consumption and its inappropriate design and management can severely affect the performance of the system. In this paper, we present a new approach to reduce the energy of the shared register file in upcoming embedded VLIW architectures with several processing units. Energy savings up to a 60% can be obtained in the register file without any performance penalty. It is based on a set of hardware extensions and a compiler-based energy-aware reg- ister assignment algorithm that enable the de/activation of parts of the register file (i.e. sub-banks) in an independent way at run-time, which can be easily included in these embedded architectures