Transport Triggered Array Processor for Vision Applications

Safarpour, Mehdi; Hautala, Ilkka; López, Miguel Bordallo; Silvén, Olli

doi:10.1007/978-3-030-27562-4_26

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Training and optimizing even a medium size neural model is computationally expensive [3], [4]. While neural inference consumes less energy, it is an equally important optimization target due to the large number of devices [5].…”

Section: Introductionmentioning

confidence: 99%

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

Safarpour

Deng

Massingham

et al. 2021

2021 IEEE Nordic Circuits and Systems Conference (NorCAS)

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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 energy hungry building blocks in embedded systems. Without an error feedback mechanism, blind voltage down-scaling will result in degraded accuracy or total system failure. In this paper, solutions based on the inherent properties of Self-Supervised Learning (SSL) and Algorithm Based Fault Tolerance (ABFT) techniques were investigated. 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 lowoverhead 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 48%, without compromising the accuracy of the model was achieved.

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Section: Introductionmentioning

confidence: 99%

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

Safarpour

Deng

Massingham

et al. 2021

2021 IEEE Nordic Circuits and Systems Conference (NorCAS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Timing uncertainties exacerbate at reduced voltages due to increased impact of process variations. Solutions based on static calibration or emulation are either too conservative, i.e., lead to unnecessary clock scaling, or do not take into account dynamic variations such as voltage noise, aging and temperature [7], [8].…”

Section: Introductionmentioning

confidence: 99%

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

Safarpour¹,

Deng²,

Massingham³

et al. 2021

Preprint

Self Cite

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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

show abstract

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

Safarpour¹,

Deng²,

Massingham³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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

show abstract

Transport Triggered Array Processor for Vision Applications

Cited by 3 publications

References 23 publications

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

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

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

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

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