Processing-in-memory (PIM) architectures are promising for accelerating intensive workloads due to their high internal bandwidth. This paper introduces a technique for accelerating Fully Homomorphic Encryption over the Torus (TFHE), a promising yet intensive application, on a realistic PIM system. Existing TFHE accelerators focus on exploiting parallelism, often overlooking data affinity, which leads to performance degradation in PIM due to excessive remote data accesses (RDAs). To address this, we present an affinity-based approach that optimizes the computation of TFHE on PIM. We apply algorithmic optimizations to TFHE, enabling PIM to effectively leverage its high internal bandwidth. We analyze the affinity patterns in the sub-tasks of TFHE and develop an offline scheduler that exploits our analysis to find optimal scheduling, minimizing RDAs while maintaining sufficient parallelism. To demonstrate the practicality of our work, we design a variant of an existing PIM-HBM device with minimal hardware modifications, and perform evaluations over a real FPGA-based PIM system. Our experiments demonstrate that our affinity-based optimizations outperform prior TFHE accelerators by 4.24-209× for real-world benchmarks.

@inproceedings{NamJOP25,
  title = {Affinity-based Optimizations for TFHE on Processing-in-DRAM},
  author = {Kevin Nam and Heonhui Jung and Hyunyoung Oh and Yunheung Paek},
  booktitle = {ASPLOS},
  year = {2025},
  doi = {10.1145/3676641.3716246}
}