AgeBalance: Low-Cost Lifetime Extension for SRAM-based PIM Accelerators

Although processing-in-memory (PIM) techniques have widely been used for deep neural networks (DNNs) acceleration, the inference performance of aged PIM-based accelerators remains to be investigated. This paper makes the first attempt to study Hot Carrier Injection (HCI) and Negative Bias Temperature Instability (NBTI) aging impacts on SRAM-based DNN accelerators, which provides a novel and unified framework, termed AgeBalance for aging detection, analysis and mitigation. First, we discuss a convenient aging detection scheme. Then, we benchmark the inference accuracy drops of DNNs running on aged SRAM-based PIM accelerators. Finally, we propose a low-cost anti-aging training method without incurring additional hardware overhead on SRAM-based DNN accelerators. Extensive experimental results on MNIST, CIFAR10 and AG News datasets show that aging can cause the inference accuracy of shallow or deep DNNs to drop to about 10%, close to random guessing. The aging mitigation scheme proposed in this paper can largely restore the accuracy to the original. Moreover, the SRAM write overhead of our method is much reduced thanks to a score-based training approach, leading to a reduction of 5× to 10× writing energy compared to the traditional training method.

Hardware and Software Information

Hardware

GPU: NVIDIA GeForce RTX 4090
CPU: AMD EPYC 7542 32-Core Processor

Software

Python version:  3.10.16 (main, Dec 11 2024, 16:24:50) [GCC 11.2.0]
torch version:  2.7.1+cu126
numpy version:  1.26.4
matplotlib version:  3.10.3
sklearn version:  1.7.2
CUDA is available
CUDA version:  12.6

Binary ResNet

bash run_binary_train.sh

Score-based training for NBTI and HCI

bash run_score.sh