摘要
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.
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.
