A DRAM-Based Processing-in-Memory Accelerator for Privacy-Protecting Machine Learning

The sensational outcomes of machine learning (ML) are witnessed. As the big success relies on continual training with massive data encompassing sensitive information, deep neural network (DNN) models easily leak private information. For instance, large pre-trained language models contain a substantial volume of private information, which can be acquired by querying with appropriate prompts. This raises concerns regarding privacy in ML, and DNN models are evolving for privacy-preserving ML (PPML).