Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators
  through Training with Right-Censored Gaussian Noise
Zheyu Yan Yifan Qin Wujie Wen Xiaobo Sharon Hu Yiyu Shi
Compute-in-Memory (CiM), built upon non-volatile memory (NVM) devices, is
promising for accelerating deep neural networks (DNNs) owing to its in-situ
data processing capability and superior energy efficiency. Unfortunately, the
well-trained model parameters, after being mapped to NVM devices, can often
exhibit large deviations from their intended values due to device variations,
resulting in notable performance degradation in these CiM-based DNN
accelerators. There exists a long list of solutions to address this issue.
However, they mainly focus on improving the mean performance of CiM DNN
accelerators. How to guarantee the worst-case performance under the impact of
device variations, which is crucial for many safety-critical applications such
as self-driving cars, has been far less explored. In this work, we propose to
use the k-th percentile performance (KPP) to capture the realistic worst-case
performance of DNN models executing on CiM accelerators. Through a formal
analysis of the properties of KPP and the noise injection-based DNN training,
we demonstrate that injecting a novel right-censored Gaussian noise, as opposed
to the conventional Gaussian noise, significantly improves the KPP of DNNs. We
further propose an automated method to determine the optimal hyperparameters
for injecting this right-censored Gaussian noise during the training process.
Our method achieves up to a 26% improvement in KPP compared to the
state-of-the-art methods employed to enhance DNN robustness under the impact of
device variations.