VaLI: Variability-aware Fine-tuning with Low-rank Adapter and Iterative Training for ReRAM Computation-in-Memory

This paper proposes a novel algorithm called Variability-aware fine-tuning with Low-rank adapter and Iterative training (VaLI). Previous research applies variability-aware training (VAT) to tackle the difficulty in deploying AI models on non-volatile Computation-in-Memory (CiM) because of the device error introduced from non-idealities of non-volatile memory, e.g. ReRAM. To enable large AI models on the edge device, VaLI introduces Variability-Aware Fine-Tuning (VAFT) which extends the conventional VAT and saves training time. Moreover, VaLI incorporates Low-Rank Adapter (LoRA) to further reduce the excessive computation resources in the training, while proposing iterative training to improve the instability of VAFT with LoRA due to low-rank matrices. The proposed VaLI is evaluated across several models and datasets to showcase its effectiveness by reducing trainable parameters by an average of 90% while maintaining competitive model accuracy against device error.