User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse




Conference Paper

Bit Error Robustness for Energy-Efficient DNN Accelerators


Stutz,  David
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;


Schiele,  Bernt
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Stutz, D., Chandramoorthy, N., Hein, M., & Schiele, B. (in press). Bit Error Robustness for Energy-Efficient DNN Accelerators. In Proceedings of Machine Learning and Systems. mlsys.org.

Cite as: http://hdl.handle.net/21.11116/0000-0007-80D4-8
Deep neural network (DNN) accelerators received considerable attention in past years due to saved energy compared to mainstream hardware. Low-voltage operation of DNN accelerators allows to further reduce energy consumption significantly, however, causes bit-level failures in the memory storing the quantized DNN weights. In this paper, we show that a combination of robust fixed-point quantization, weight clipping, and random bit error training (RandBET) improves robustness against random bit errors in (quantized) DNN weights significantly. This leads to high energy savings from both low-voltage operation as well as low-precision quantization. Our approach generalizes across operating voltages and accelerators, as demonstrated on bit errors from profiled SRAM arrays. We also discuss why weight clipping alone is already a quite effective way to achieve robustness against bit errors. Moreover, we specifically discuss the involved trade-offs regarding accuracy, robustness and precision: Without losing more than 1% in accuracy compared to a normally trained 8-bit DNN, we can reduce energy consumption on CIFAR-10 by 20%. Higher energy savings of, e.g., 30%, are possible at the cost of 2.5% accuracy, even for 4-bit DNNs.