  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 better.
Protecting the computation of sensitive data is also an imperative yet
challenging objective; processor-supported secure enclaves serve as the key
element in confidential computing in the cloud. However, side-channel attacks
are threatening their security boundaries. The current processor architectures
consume a considerable portion of its cycles in moving data. Near data
computation is a promising approach that minimizes redundant data movement by
placing computation inside storage. In this paper, we present a novel design
for Processing-In-Memory (PIM) as a data-intensive workload accelerator for
confidential computing. 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 \emph{inside} memory. We then explain our security model and
programming model developed for PIM-based computation offloading. We construct
our findings into a software-hardware co-design, which we call PIM-Enclave. Our
design illustrates the advantages of PIM-based confidential computing
acceleration. Our evaluation shows PIM-Enclave can provide a side-channel
resistant secure computation offloading and run data-intensive applications
with negligible performance overhead compared to baseline PIM model.
