scholarly journals Low-Voltage Energy Efficient Neural Inference by Leveraging Fault Detection Techniques

2021 ◽  
Author(s):  
Mehdi Safarpour ◽  
Tommy Z. Deng ◽  
John Massingham ◽  
Lei Xun ◽  
Mohammad Sabokrou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Energy Efficient ◽  
Low Voltage ◽  
Detection Techniques

scholarly journals Low-Voltage Energy Efficient Neural Inference by Leveraging Fault Detection Techniques

2021 ◽  
Author(s):  
Mehdi Safarpour ◽  
Tommy Z. Deng ◽  
John Massingham ◽  
Lei Xun ◽  
Mohammad Sabokrou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Error Detection ◽  
Energy Savings ◽  
Low Voltage ◽  
Building Blocks ◽  
Feedback Mechanism ◽  
Total System ◽  
Data Set ◽  
Detection Techniques ◽  
Energy Efficiency Improvement

This paper presents simple techniques to significantly reduced energy consumption of DNNs: Operating at reduced voltages offers substantial energy efficiency improvement but at the expense of increasing the probability of computational errors due to hardware faults. In this context, we targeted Deep Neural Networks (DNN) as emerging energy hungry building blocks in embedded applications. Without an error feedback mechanism, blind voltage down-scaling will result in degraded accuracy or total system failure. To enable safe voltage down-scaling, in this paper two solutions based on Self-Supervised Learning (SSL) and Algorithm Based Fault Tolerance (ABFT) were developed. A DNN model trained on MNIST data-set was deployed on a Field Programmable Gate Array (FPGA) that operated at reduced voltages and employed the proposed schemes. The SSL approach provides extremely low-overhead (≈0.2%) fault detection at the cost of lower error coverage and extra training, while ABFT incurs less than 8%overheads at run-time with close to 100% error detection rate. By using the solutions, substantial energy savings, i.e., up to 40.3%,without compromising the accuracy of the model was achieved

scholarly journals Low-Voltage Energy Efficient Neural Inference by Leveraging Fault Detection Techniques

2021 ◽  
Author(s):  
Mehdi Safarpour ◽  
Tommy Z. Deng ◽  
John Massingham ◽  
Lei Xun ◽  
Mohammad Sabokrou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Error Detection ◽  
Energy Savings ◽  
Low Voltage ◽  
Building Blocks ◽  
Feedback Mechanism ◽  
Total System ◽  
Data Set ◽  
Detection Techniques ◽  
Energy Efficiency Improvement

Operating at reduced voltages offers substantial energy efficiency improvement but at the expense of increasing the probability of computational errors due to hardware faults. In this context, we targeted Deep Neural Networks (DNN) as emerging energy hungry building blocks in embedded applications. Without an error feedback mechanism, blind voltage down-scaling will result in degraded accuracy or total system failure. To enable safe voltage down-scaling, in this paper two solutions based on Self-Supervised Learning (SSL) and Algorithm Based Fault Tolerance (ABFT) were developed. A DNN model trained on MNIST data-set was deployed on a Field Programmable Gate Array (FPGA) that operated at reduced voltages and employed the proposed schemes. The SSL approach provides extremely low-overhead (≈0.2%) fault detection at the cost of lower error coverage and extra training, while ABFT incurs less than 8%overheads at run-time with close to 100% error detection rate. By using the solutions, substantial energy savings, i.e., up to 40.3%,without compromising the accuracy of the model was achieved

scholarly journals Low-Voltage Energy Efficient Neural Inference by Leveraging Fault Detection Techniques

2021 ◽  
Author(s):  
Mehdi Safarpour ◽  
Tommy Z. Deng ◽  
John Massingham ◽  
Lei Xun ◽  
Mohammad Sabokrou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Error Detection ◽  
Energy Savings ◽  
Low Voltage ◽  
Building Blocks ◽  
Feedback Mechanism ◽  
Total System ◽  
Data Set ◽  
Detection Techniques ◽  
Energy Efficiency Improvement

Operating at reduced voltages offers substantial energy efficiency improvement but at the expense of increasing the probability of computational errors due to hardware faults. In this context, we targeted Deep Neural Networks (DNN) as emerging energy hungry building blocks in embedded applications. Without an error feedback mechanism, blind voltage down-scaling will result in degraded accuracy or total system failure. To enable safe voltage down-scaling, in this paper two solutions based on Self-Supervised Learning (SSL) and Algorithm Based Fault Tolerance (ABFT) were developed. A DNN model trained on MNIST data-set was deployed on a Field Programmable Gate Array (FPGA) that operated at reduced voltages and employed the proposed schemes. The SSL approach provides extremely low-overhead (≈0.2%) fault detection at the cost of lower error coverage and extra training, while ABFT incurs less than 8%overheads at run-time with close to 100% error detection rate. By using the solutions, substantial energy savings, i.e., up to 40.3%,without compromising the accuracy of the model was achieved

Fault Detection for Low-Voltage Residential Distribution Systems With Low-Frequency Measured Data

2020 ◽  
Vol 14 (4) ◽  
pp. 5265-5273
Author(s):  
Mehdi Shafiei ◽  
Faranak Golestaneh ◽  
Gerard Ledwich ◽  
Ghavameddin Nourbakhsh ◽  
Hoay Beng Gooi ◽  
...  
Keyword(s):  
Fault Detection ◽  
Distribution Systems ◽  
Low Voltage ◽  
Low Frequency ◽  
Measured Data ◽  
Residential Distribution

scholarly journals ENERGY EFFICIENCY IN RESIDENTIAL BUILDINGS, LESS STRAIGHT FORWARD THEN PRESUMED

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Hugo Hens
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Energy Use ◽  
Low Voltage ◽  
Residential Buildings ◽  
Primary Energy ◽  
Heating And Cooling ◽  
Net Zero ◽  
Energy Conserving ◽  
New Construction

Since the 1990s, the successive EU directives and related national or regional legislations require new construction and retrofits to be as much as possible energy-efficient. Several measures that should stepwise minimize the primary energy use for heating and cooling have become mandated as requirement. However, in reality, related predicted savings are not seen in practice. Two effects are responsible for that. The first one refers to dweller habits, which are more energy-conserving than the calculation tools presume. In fact, while in non-energy-efficient ones, habits on average result in up to a 50% lower end energy use for heating than predicted. That percentage drops to zero or it even turns negative in extremely energy-efficient residences. The second effect refers to problems with low-voltage distribution grids not designed to transport the peaks in electricity whensunny in summer. Through that, a part of converters has to be uncoupled now and then, which means less renewable electricity. This is illustrated by examples that in theory should be net-zero buildings due to the measures applied and the presence of enough photovoltaic cells (PV) on each roof. We can conclude that mandating extreme energy efficiency far beyond the present total optimum value for residential buildings looks questionable as a policy. However, despite that, governments and administrations still seem to require even more extreme measurements regarding energy efficiency.

New Preemptive Arc-Fault Detection Techniques in Medium-Voltage Switchgear and Motor Controls

2016 ◽  
Vol 52 (1) ◽  
pp. 740-750 ◽  
Author(s):  
John A. Kay ◽  
G. Amjad Hussain ◽  
Matti Lehtonen ◽  
Lauri Kumpulainen
Keyword(s):  
Fault Detection ◽  
Medium Voltage ◽  
Detection Techniques ◽  
Motor Controls ◽  
Medium Voltage Switchgear
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