Cost- and Dataset-free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators
Giju Jung1, Mohammed Fouda2, Sugil Lee1, Jongeun Lee1, Ahmed Eltawil2,3 and Fadi Kurdahi2
1Dept. of Electrical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea
2Center for Embedded & Cyber-physical Systems, University of California–Irvine, CA, USA
3CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
ABSTRACT
Resistive RAMs can implement extremely efficient matrix vector multiplication, drawing much attention for deep learning accelerator research. However, high fault rate is one of the fundamental challenges of ReRAM crossbar array-based deep learning accelerators. In this paper we propose a datasetfree, cost-free method to mitigate the impact of stuck-at faults in ReRAM crossbar arrays for deep learning applications. Our technique exploits the statistical properties of deep learning applications, hence complementary to previous hardware or algorithmic methods. Our experimental results using MNIST and CIFAR-10 datasets in binary networks demonstrate that our technique is very effective, both alone and together with previous methods, up to 20% fault rate, which is higher than the previous remapping methods. We also evaluate our method in the presence of other non-idealities such as variability and IR drop.
Keywords: Artificial Neural Networks (ANNs), ReRAM Crossbar Array, Stuck-at Fault, Batch Normalization.
