DPIMA: A Differentially Private Processing-in-DRAM Accelerator

The rapid success of machine learning (ML) has accelerated its deployment in a wide range of sensitive domains, raising critical concerns about data privacy. Privacy-preserving ML (PPML) has, therefore, emerged as a fundamental requirement, with differential privacy (DP) providing a rigorous mathematical framework to guarantee individual privacy while enabling meaningful learning. However, applying DP to deep learning imposes substantial computational and memory overhead due to noise injection, privacy accounting, and additional data manipulation steps, which are not well supported by existing ML hardware accelerators. To address this challenge, we proposeDPIMA, a novel DP-guaranteed processing-in-memory (PIM) accelerator designed specifically to support efficient DP training. DPIMA leverages the intrinsic parallelism and bandwidth of DRAM-based PIM while introducing architectural innovations, including a full-operation adder tree (FOAT) and a system-levelprivacy-aware dataflowthat jointly optimize the execution of DP-specific operations. Through a comprehensive set of experiments across diverse deep learning models and training configurations, DPIMA demonstrates up to$13.8\times $speedup and$123.8\times $energy efficiency improvement over the baseline design for DP training. These results highlight the effectiveness of codesigning privacy mechanisms with memory-centric hardware, paving the way for scalable and efficient privacy-preserving learning systems.