AI Signal 454
Google's HEIR compiles pre-trained AI models to run on encrypted data
↗
Google introduced HEIR, an open-source compiler toolchain that adapts pre-trained AI models to process homomorphic-encrypted inputs instead of unencrypted data.
HEIR lowers the barrier to deploying homomorphic encryption by letting developers compile existing models rather than building new ones for encrypted data. However, performance overhead remains substantial, with operations running roughly 1000x slower, and the compilation process is not yet fully automated.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
HEIR compiles pre-trained AI models built for unencrypted inputs to operate on encrypted data using an intermediate representation abstraction layer.
Developers write Python programs, annotate encrypted data types, and compile via HEIR, though the process currently requires manual steps including torch_mlir export to MLIR.
Performance overhead is significant, with reported latencies of 80ms for 64-bit equality operations and up to 8 seconds for division, though LLM workloads may fare better due to their reliance on addition and multiplication.
THE CLUSTER