Confidential Computing with Heterogeneous Devices at Cloud-Scale

Aritra Dhar, Supraja Sridhara, Shweta Shinde, Srdjan Capkun, Renzo Andri

Abstract: Cloud-centric workloads increasingly leverage domain-specific accelerators (DSAs) such as GPU, NPU, FPGA, etc., to achieve massive speedup over general-purpose CPUs. These workloads compute sensitive data; furthermore, the programs can be proprietary business secrets such as high-performance AI models. Therefore, several confidential cloud solutions have recently emerged to protect against the attacker-controlled software stack (OS/VMM) and the cloud service providers or CSPs themselves. CPU-centric trusted execution environments, or TEEs, have been around for decades and are deployed commercially. However, despite some recent proposals, most nodes lack TEE capability and, therefore, are unprotected against malicious CSP and software stack.We address this gap by proposing a new dedicated hardware module, the security controller (SC), that acts as the TEE proxy for the legacy non-TEE DSA nodes in a data center across racks. SC enforces access control and attestation mechanisms and protects the non-TEE nodes even from a physical attacker. This way, SC enables new-generation TEE-enabled nodes and legacy non-TEE nodes to be used in a data center simultaneously while ensuring security. We implement and synthesize SC hardware and evaluate it with real-world cloud-centric workloads with heterogeneous DSAs. Our evaluation shows that, on average, SC introduces 1.5-5% overhead while running AI, Redis, and file system workloads and scales well with an increasing number of DSA nodes (up to 2236 concurrent NPUs running CNNs).