SoftVN

Abstract

Trusted execution environments (TEEs) in processors protect off-chip memory (DRAM), and ensure its confidentiality and integrity using memory encryption and integrity verification. However, such memory protection can incur significant performance overhead as it requires additional memory accesses for protection metadata such as version numbers (VNs) and MACs. This paper proposes SoftVN, an extension to the current memory protection schemes, which significantly reduces the overhead of today's state-of-the-art by allowing software to provide VNs for memory accesses. For memory-intensive applications with simple memory access patterns for large data structures, the VNs only need to be maintained for data structures instead of individual cache blocks and can be tracked in software with low efforts. Off-chip VN accesses for memory reads can be removed if they are tracked and provided by software. We evaluate SoftVN by simulating a diverse set of memory-intensive applications, including deep learning, graph processing, and bioinformatics algorithms. The experimental results show that SoftVN reduces the memory protection overhead by 82% compared to the baseline similar to Intel SGX, and improves the performance by 33% on average. The maximum performance improvement can be as high as 65%.