Back to Search
Start Over
Variability-Aware Training and Self-Tuning of Highly Quantized DNNs for Analog PIM
- Publication Year :
- 2021
-
Abstract
- DNNs deployed on analog processing in memory (PIM) architectures are subject to fabrication-time variability. We developed a new joint variability- and quantization-aware DNN training algorithm for highly quantized analog PIM-based models that is significantly more effective than prior work. It outperforms variability-oblivious and post-training quantized models on multiple computer vision datasets/models. For low-bitwidth models and high variation, the gain in accuracy is up to 35.7% for ResNet-18 over the best alternative. We demonstrate that, under a realistic pattern of within- and between-chip components of variability, training alone is unable to prevent large DNN accuracy loss (of up to 54% on CIFAR-100/ResNet-18). We introduce a self-tuning DNN architecture that dynamically adjusts layer-wise activations during inference and is effective in reducing accuracy loss to below 10%.<br />Comment: This is the preprint version of our paper accepted in DATE 2022
- Subjects :
- Computer Science - Machine Learning
Computer Science - Emerging Technologies
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2111.06457
- Document Type :
- Working Paper