Confidential Machine Learning within Graphcore IPUs

We present IPU Trusted Extensions (ITX), a set of experimental hardware extensions that enable trusted execution environments in Graphcore's AI accelerators. ITX enables the execution of AI workloads with strong confidentiality and integrity guarantees at low performance overheads. ITX isolates workloads from untrusted hosts, and ensures their data and models remain encrypted at all times except within the IPU. ITX includes a hardware root-of-trust that provides attestation capabilities and orchestrates trusted execution, and on-chip programmable cryptographic engines for authenticated encryption of code and data at PCIe bandwidth. We also present software for ITX in the form of compiler and runtime extensions that support multi-party training without requiring a CPU-based TEE. Experimental support for ITX is included in Graphcore's GC200 IPU taped out at TSMC's 7nm technology node. Its evaluation on a development board using standard DNN training workloads suggests that ITX adds less than 5% performance overhead, and delivers up to 17x better performance compared to CPU-based confidential computing systems relying on AMD SEV-SNP.

Visor: Privacy-Preserving Video Analytics as a Cloud Service

Video-analytics-as-a-service is becoming an important offering for cloud providers. A key concern in such services is privacy of the videos being analyzed. While trusted execution environments (TEEs) are promising options for preventing the direct leakage of private video content, they remain vulnerable to side-channel attacks. We present Visor, a system that provides confidentiality for the user's video stream as well as the ML models in the presence of a compromised cloud platform and untrusted co-tenants. Visor executes video pipelines in a hybrid TEE that spans both the CPU and GPU. It protects the pipeline against side-channel attacks induced by data-dependent access patterns of video modules, and also addresses leakage in the CPU-GPU communication channel. Visor is up to $1000\times$ faster than na\"ive oblivious solutions, and its overheads relative to a non-oblivious baseline are limited to $2\times$--$6\times$.