What HEIR Adds to Homomorphic Encryption
Google announced HEIR on August 14, 2026 as an open-source compiler toolchain that converts trained AI models into versions capable of running inference on encrypted data. The system, described in a post by staff software engineer Jeremy Kun, targets fully homomorphic encryption so that a server can perform computations without ever accessing plaintext inputs or outputs.
HEIR supplies an intermediate representation layer that standardizes the translation of model operations into encrypted equivalents. This addresses a long-standing gap between cryptographic primitives and production deployment. Without such tooling, teams had to hand-craft low-level circuits for each model, a process that limited experimentation to specialized research groups.
The compiler integrates into Google's Private Computing Toolkit and accepts existing trained models as input. It emits code that executes under homomorphic encryption schemes while preserving the original accuracy. This automation removes the need for manual circuit design, which previously blocked wider adoption.
HEIR does not change the underlying performance characteristics of homomorphic encryption. Benchmarks cited in the announcement show overheads between 100x and 1,000x compared with plaintext inference, and some demonstrations still require minutes per query on CPU hardware. The contribution lies in reducing engineering friction rather than eliminating computational cost.
By releasing the compiler as open source, Google positions HEIR as infrastructure that ordinary teams can integrate into existing pipelines. Details on supported model architectures and integration patterns continue to emerge from the initial release.
How the Compiler Converts Pre-Trained Models
Google's HEIR compiler automates the process of adapting pre-trained models for inference over encrypted data. Previously this step required manual rewriting by specialists familiar with the details of homomorphic encryption schemes. The compiler accepts a standard trained model and produces an equivalent version that operates directly on ciphertext, preserving the original model weights and architecture while inserting the necessary encryption-aware operations.
The project description from the Google security blog states that HEIR, which stands for Homomorphic Encryption Intermediate Representation, targets exactly this automation. Staff Software Engineer Jeremy Kun described the goal as creating a pathway that removes the need for cryptography expertise at each deployment. In practice the compiler handles the mapping of arithmetic operations to their encrypted counterparts and manages the intermediate representations required for correct evaluation under encryption.
Details on the precise sequence of passes, intermediate representations, or supported model formats remain limited in the initial announcement. The available information focuses on the outcome, namely that a pre-trained model can be processed without hand-coded adjustments for each new architecture. Some inference runs still require minutes on CPU hardware, reflecting the computational overhead that the compiler itself does not eliminate.
Current Performance Numbers and Overhead
No concrete benchmarks or overhead figures appear in the initial HEIR announcement. The project documentation focuses on the compiler pipeline rather than measured latency or throughput for specific models. Details on runtime costs for encrypted inference remain limited to the general characteristics of fully homomorphic encryption.
The technical community has already flagged one structural limit that affects any compiler built on this approach. Branching operations remain expensive under current FHE schemes, which restricts the class of models that can run efficiently without manual restructuring. This constraint is described as the practical ceiling for HEIR's scope rather than a temporary implementation detail.
Google positions the open-source release as infrastructure that other teams can extend. Accelerator vendors and rival clouds now share a common target, yet the absence of published numbers means adopters must generate their own measurements before assessing production viability. Further reports on overhead are expected as external contributors test the compiler against standard model architectures.
Differences from End-to-End Encryption
Google's HEIR compiler applies homomorphic encryption so that AI models can execute inference on ciphertexts and return results that remain encrypted throughout. Servers process the data without access to plaintext values at any stage. This approach differs from conventional encryption schemes, where data must be decrypted before computation occurs on the server side.
The announcement states that servers can process ciphertexts and return encrypted results without exposing any underlying information. HEIR supplies an intermediate representation and toolchain that lowers the expertise required to implement these operations for models such as recommendation systems and hotword detectors. Demos on single-threaded CPUs illustrate the current performance characteristics for four sample applications.
Traditional end-to-end encryption protects data during transmission and storage yet requires decryption prior to model evaluation. Homomorphic methods remove that decryption step for the computation itself. The research material does not supply explicit benchmarks that contrast HEIR latency against standard encrypted pipelines, and details on this are still emerging. Google developed the project in collaboration with hardware partners and universities to address prior usability barriers that limited homomorphic encryption to specialist teams.
The compiler focuses on practical private inference rather than general encrypted communication protocols. Further engineering work will determine how these techniques integrate with existing deployment stacks that already rely on transport-layer encryption.
Integration Steps for Engineering Teams
HEIR is released as an open-source compiler toolchain that converts pre-trained AI models operating on unencrypted data to versions that accept encrypted inputs. The project addresses the historical requirement that teams include cryptographers to adapt programs for homomorphic encryption. Google positions the compiler within its Private Computing Toolkit as a step toward broader cryptographic privacy tools that do not rely on hardware enclaves.
Specific commands, configuration files, or deployment sequences for production environments are not yet documented in the initial announcement. The stated vision is a one-click workflow that lets non-experts add encrypted inference to existing applications, but current users must examine the compiler source and intermediate representation directly. Details on this are still emerging as the project matures beyond its initial release.
Engineering teams interested in early adoption can therefore begin by reviewing the open-source repository to understand how the toolchain maps standard model operations to homomorphic equivalents. Further guidance on packaging, performance tuning, and integration with serving infrastructure will depend on subsequent documentation and examples released by the maintainers.
Supported Operations and Model Types
HEIR compiles pre-trained models to execute inference under homomorphic encryption schemes that include BGV, BFV, and CKKS. The compiler targets these schemes through backends such as OpenFHE and Lattigo, both integrated via its MLIR foundation. This setup allows the resulting programs to perform arithmetic directly on ciphertexts without exposing plaintext values at any point during execution.
The four published demonstrations illustrate the range of models that can be handled today. A recommendation system shows support for embedding lookups and matrix multiplications under encryption. Card fraud detection and intrusion detection workloads indicate that classification models, likely linear or low-degree polynomial forms, can be converted and run on encrypted transaction or network data. Hotword spotting adds an audio inference example, confirming that simpler sequential or convolutional patterns are also feasible once compiled.
Manual conversion of arbitrary programs to these schemes still demands cryptographic expertise, which limits immediate applicability. HEIR reduces that barrier by automating the translation step, yet the current release does not enumerate every supported layer type or activation function. Details on broader model families, such as large transformers or deeper networks, remain limited to what the four demos establish.
Remaining Barriers to Production Use
Google's release of HEIR addresses the historical requirement for manual cryptographic tuning, yet measured performance remains a constraint. The toolchain compiles a TensorFlow Lite model to fully homomorphic encryption and produces private inference results in 16 seconds for a 3-layer neural network. That latency figure, drawn directly from the company's announcement, indicates current overheads that exceed typical production service-level expectations for interactive workloads.
The project supports BGV, BFV, and CKKS schemes through OpenFHE and Lattigo backends, all built on MLIR and released under Apache-2.0. These choices enable interoperability across schemes and accelerators, but they do not yet eliminate the need for careful scheme selection and parameter tuning per model. The four published demonstrations cover recommendations, card fraud detection, intrusion detection, and hotword spotting, yet none report throughput or accuracy at the scale of production datasets.
Details on compilation time for deeper networks, memory consumption under realistic batch sizes, and integration with existing ML pipelines are still emerging. Until those measurements appear in engineering reports or follow-on papers, teams evaluating HEIR for health records or payment streams will continue to treat the 16-second baseline as the clearest available signal of remaining practical limits.
Open-Source Release and Toolkit Contents
Google has released Homomorphic Encryption Intermediate Representation, or HEIR, as an open-source compiler toolchain for homomorphic encryption. The project builds directly on MLIR and targets interoperability across different FHE schemes, compilers, and hardware accelerators. Its stated goal is to reduce repeated work in privacy engineering and research by supplying shared infrastructure rather than leaving each team to construct its own base layers.
HEIR organizes computation into multiple layers of abstraction. Compiler passes move programs downward through these layers, exposing opportunities for targeted optimizations at each stage. The design explicitly supports work on ciphertext packing, parameter selection, and arithmetization. Researchers can therefore insert new passes without rebuilding parsers, syntax handling, or standard lowering steps.
The toolkit supplies an existing syntax, parsers, intermediate representations, and a collection of standard compiler passes. It also includes multiple backend targets. This structure lets contributors concentrate on novel optimizations, benchmark comparisons, or hardware-specific improvements instead of recreating common components. The project maintains regular meetings open to interested developers, with schedules listed on the HEIR website, and is positioned as a potential shared hub for both academic and industry participants working on FHE tooling.
Example Inference Workflow with HEIR
HEIR supports research into homomorphic encryption by supplying syntax, parsers, intermediate representations, standard compiler passes, and multiple backend targets. Researchers can therefore direct their efforts toward novel optimizations rather than rebuilding these foundations each time. The framework also includes a plaintext backend that executes operations in the unencrypted domain of a given scheme. This target serves as a reference implementation for spotting errors during compilation and helps determine appropriate parameters such as BGV or BFV plaintext moduli and CKKS scaling factors, tasks that depend on accurate bounds for intermediate values.
An inference workflow begins with a model expressed in the supported syntax. Standard passes then transform the representation through arithmetization and related stages before targeting a chosen backend. The plaintext backend permits verification of numerical behavior without encryption overhead, confirming that value ranges remain within expected limits. Once parameters are validated, the same pipeline can switch to an encrypted backend for actual secure execution. Details on complete end-to-end examples that combine these steps with specific AI models remain limited in current documentation.