SE-PIM: In-Memory Acceleration of Data-Intensive Confidential Computing

Demand for data-intensive workloads and confidential computing are the prominent research directions shaping the future of cloud computing. Computer architectures are evolving to accommodate the computing of large data. Meanwhile, a plethora of works has explored protecting the confidentiality of the in-cloud computation in the context of hardware-based secure enclaves. However, the approach has faced challenges in achieving efficient large data computation. In this article, we present a novel design, called se-pim, that retrofits Processing-In-Memory (PIM) as a data-intensive confidential computing accelerator. PIM-accelerated computation renders large data computation highly efficient by minimizing data movement. Based on our observation that moving computation closer to memory can achieve efficiency of computation and confidentiality of the processed information simultaneously, we study the advantages of confidential computing inside memory. We construct our findings into a software-hardware co-design called se-pim. Our design illustrates the advantages of PIM-based confidential computing acceleration. We study the challenges in adapting PIM in confidential computing and propose a set of imperative changes, as well as a programming model that can utilize them. Our evaluation shows se-pim can provide a side-channel resistant secure computation offloading and run data-intensive applications with negligible performance overhead compared to the baseline PIM model.

Automated summary

Demand for data-intensive workloads and confidential computing is shaping the future of cloud computing. In this article, a novel design called se-pim is presented, which utilizes Processing-In-Memory (PIM) as a data-intensive confidential computing accelerator. By bringing computation closer to memory, se-pim achieves highly efficient large data computation. The evaluation shows that se-pim provides secure computation offloading with negligible performance overhead compared to the baseline PIM model.