Confidential Neural Computing - hosting generative AI model workloads in a Trusted Execution Environment
Abstract
As generative AI models grow more & more capable, products increasingly want to leverage these models to provide personalized generative experiences for their users. This personalization relies on fine-tuning and running these models with sensitive user data. The sensitivity of this user data motivates the need to train & run these models in a privacy-safe way that provides strong safety guarantees to the user, and earns user trust.
The Confidential Neural Computing project builds an ML framework focused on enabled generative AI training and inference in secure enclaves. In this talk, we give an overview of some of the core components of the Confidential Neural Computing framework, explain how the framework leverages current CPU & GPU confidential computing technologies, and share updates from our current & on-going areas of investigation.
The Confidential Neural Computing project builds an ML framework focused on enabled generative AI training and inference in secure enclaves. In this talk, we give an overview of some of the core components of the Confidential Neural Computing framework, explain how the framework leverages current CPU & GPU confidential computing technologies, and share updates from our current & on-going areas of investigation.
