Welcome back, future privacy architect! In our previous chapters, we explored the fascinating world of homomorphic encryption (HE) and fully homomorphic encryption (FHE), understanding their power to enable secure computation on encrypted data. We also introduced HEIR, an open-source compiler framework designed to make building FHE-powered applications more accessible.

Today, we’re rolling up our sleeves to perform our very first encrypted computation using HEIR. Think of this as our “Hello World” moment for private AI. While HEIR is still in active development, we’ll set up a basic HEIR environment and walk through a simple, illustrative example to see FHE compilation in action. This hands-on experience will solidify your understanding of how HEIR translates abstract FHE principles into practical, privacy-preserving operations, even if the end-to-end execution aspects are still evolving.

HEIR’s Role in the FHE Ecosystem

Before we dive into code, let’s quickly recap HEIR’s purpose. HEIR isn’t an FHE library itself; rather, it’s a compiler framework. This means it takes a description of a computation (often in an Intermediate Representation like MLIR), understands which parts need to operate on encrypted data, and then transforms that description into a form that can be executed by an FHE backend.

The goal is to streamline the complex process of FHE programming. Instead of manually handling low-level cryptographic operations, developers can define their desired computation, and HEIR handles the heavy lifting of compiling it into an FHE-compatible sequence. This greatly reduces the expertise required to build privacy-preserving applications.

Here’s a simplified view of the HEIR compilation process:

flowchart TD A[User FHE Program] --> B[HEIR Compiler Front-End] B --> C[HEIR Intermediate Representation] C --> D[Backend Specific FHE IR] D --> E[FHE Executable] E --> F[Private AI Inference]

📌 Key Idea: HEIR is a compiler framework, not a standalone FHE library. It bridges the gap between high-level computation descriptions and low-level FHE operations.

Setting Up Your HEIR Workbench

As of 2026-08-18, HEIR is an active research project, and its development is ongoing. This means specific stable releases with version numbers aren’t widely published in the traditional sense. Instead, developers typically work with the main branch of the GitHub repository. It’s an open-source project developed by a team of researchers and engineers.

Prerequisites: CMake

HEIR uses CMake to manage its build process. You’ll need CMake installed on your system.

  1. Check CMake Version: Open your terminal and run:

    cmake --version

    You should see output similar to cmake version 3.20.0 or newer. If CMake isn’t installed or is an older version, you’ll need to install it. For Linux, sudo apt install cmake (Debian/Ubuntu) or sudo dnf install cmake (Fedora) usually works. For macOS, brew install cmake (with Homebrew) is common.

    ⚡ Quick Note: We recommend CMake version 3.20 or higher for best compatibility with modern C++ projects.

Step 1: Clone the HEIR Repository

First, let’s get the HEIR source code. We’ll clone the official GitHub repository.

git clone https://github.com/heir-compiler/HEIR.git

This command downloads the entire HEIR project into a new directory named HEIR in your current location.

Step 2: Navigate and Create a Build Directory

Move into the newly cloned directory and create a build subdirectory. This is a standard practice for CMake projects to keep build artifacts separate from source code.

cd HEIR
mkdir build
cd build

Step 3: Configure and Build HEIR

Now, we’ll use CMake to configure the build system and then compile HEIR.

  1. Configure with CMake: From inside your build directory, run CMake:

    cmake ..

    The .. tells CMake to look for the CMakeLists.txt file in the parent directory (the root of the HEIR repository). CMake will analyze your system and generate build files (e.g., Makefiles on Linux/macOS).

    ⚠️ What can go wrong: If you encounter errors during this step, it’s often due to missing dependencies (like specific C++ compilers, libraries, or Python packages). Check the HEIR GitHub repository’s README.md for detailed dependency instructions if you run into issues.

  2. Build HEIR: Once CMake configuration is successful, compile the project:

    cmake --build . --parallel $(nproc)
    • cmake --build . instructs CMake to build the project in the current directory.
    • --parallel $(nproc) (or -j followed by a number, e.g., -j8) tells the build system to use multiple CPU cores, which significantly speeds up compilation. $(nproc) automatically detects the number of available cores on Linux. On macOS, you might use sysctl -n hw.ncpu to get the core count.

    This process can take a significant amount of time, depending on your system’s specifications. Grab a coffee!

    ⚡ Real-world insight: Building complex compiler frameworks like HEIR from source is a common task in research and development environments. It ensures you have the latest features and can customize the build if needed.

Crafting Your First FHE Program: Adding a Constant

Since HEIR is a compiler, our “Hello World” won’t be a simple print() statement. Instead, we’ll define a very basic computation that HEIR can process. For this example, we’ll define a function that takes an encrypted input x and adds a plaintext constant 5 to it, producing an encrypted output x + 5.

HEIR typically works with an Intermediate Representation (IR), often based on MLIR (Multi-Level IR). While writing full MLIR code can be complex, we can represent our simple operation conceptually.

Let’s create a file named add_constant.mlir in the root HEIR directory (or a sub-directory you create for examples).

// add_constant.mlir
func.func @add_constant(%arg0: tensor<1xi32>) -> tensor<1xi32> {
  // Define a constant value to add
  %c5 = arith.constant 5 : i32
  %c5_tensor = tensor.from_elements %c5 : tensor<1xi32>

  // Perform the addition on the encrypted input and the constant
  %0 = arith.addi %arg0, %c5_tensor : tensor<1xi32>

  // Return the encrypted result
  func.return %0 : tensor<1xi32>
}

Let’s break down this conceptual MLIR snippet:

  • func.func @add_constant(...): This declares a function named add_constant.
  • %arg0: tensor<1xi32>: This specifies that our function takes one argument, %arg0, which is conceptually a 1-element tensor of 32-bit integers. In an FHE context, this arg0 would represent our encrypted input.
  • -> tensor<1xi32>: The function returns a 1-element tensor of 32-bit integers, representing our encrypted output.
  • %c5 = arith.constant 5 : i32: We define a constant integer value 5.
  • %c5_tensor = tensor.from_elements %c5 : tensor<1xi32>: We convert our constant 5 into a 1-element tensor, matching the input type for our addi operation.
  • %0 = arith.addi %arg0, %c5_tensor : tensor<1xi32>: This is the core operation. arith.addi performs an integer addition. It adds our encrypted input (%arg0) to our plaintext constant (%c5_tensor). Crucially, HEIR’s job is to ensure this addition happens correctly while both are encrypted (or one is plaintext but handled securely by the FHE scheme).
  • func.return %0: The function returns the result of the addition.

🧠 Important: This MLIR snippet is a simplified representation. In a real HEIR workflow, there would be more specific FHE-related operations and types to explicitly denote encrypted values and operations. The purpose here is to illustrate the computation HEIR would process.

Compiling with HEIR’s heir-opt Utility

As noted in the HEIR GitHub README, the integration between the middle-end and back-end for generating full executables is still evolving. For practical experimentation, HEIR provides a helper utility: heir-opt. This tool helps process and prepare FHE programs by applying various optimization and transformation passes.

From the HEIR/build directory, you can invoke HEIR’s tools. Let’s process our add_constant.mlir through a simplified HEIR pipeline.

First, ensure you are in the HEIR/build directory.

cd path/to/HEIR/build

Now, let’s use the HEIR compiler to process our MLIR file.

./bin/heir-opt ../add_constant.mlir --heir-simplify-arith -o add_constant_processed.mlir

Let’s break this down:

  • ./bin/heir-opt: This is the HEIR MLIR optimizer utility we just built.
  • ../add_constant.mlir: This specifies our input file. The ../ is because we’re running it from the build directory.
  • --heir-simplify-arith: This is an example of a pass HEIR might apply. It simplifies arithmetic operations. HEIR has many such passes that transform and optimize the IR for FHE.
  • -o add_constant_processed.mlir: This specifies the output file where the processed MLIR will be written.

If successful, you will find a new file add_constant_processed.mlir in your build directory. Its content might look very similar to your input for such a simple pass, but in more complex scenarios, you’d see significant transformations as HEIR prepares the program for an FHE backend.

Regarding actual execution and the format_assistant/h mention in the README: The HEIR project currently focuses on the compilation framework and intermediate transformations. The format_assistant/h reference implies a specific path or tool to get an executable if one is available or desired for a particular FHE backend. As the backend integration is in flux, direct end-to-end execution from a simple MLIR file to a numeric result is not yet a ready-to-use feature for a “Hello World” in the traditional sense.

Instead, the “Hello World” here is successfully compiling and transforming your FHE program’s representation using HEIR’s tooling. This demonstrates that HEIR can parse, understand, and apply transformations to your FHE-aware code.

🔥 Optimization / Pro tip: Understanding the output of heir-opt with different passes is key to debugging and optimizing your FHE programs. Each pass performs a specific transformation, moving the program closer to an FHE-compatible form.

Mini-Challenge: Double the Constant!

Let’s make a small modification to our add_constant.mlir file.

Challenge: Modify add_constant.mlir to add 10 instead of 5 to the encrypted input. Then, re-run the heir-opt command to process the updated file.

Hint: Look for the arith.constant operation in your add_constant.mlir file and change the literal value.

What to observe/learn: This challenge reinforces your understanding of how to define literal values in the MLIR-like syntax and how HEIR’s heir-opt tool processes your changes. It shows the iterative nature of compiler development – defining your program, compiling, and checking the output.

Common Pitfalls & Troubleshooting

  1. CMake Configuration Errors:

    • Issue: CMake Error at CMakeLists.txt:X (find_package): Could not find a package configuration file...
    • Cause: Missing development libraries (e.g., LLVM, Clang, specific Python headers) or an outdated CMake version.
    • Solution: Ensure you have all required dependencies installed as per the HEIR GitHub README. Update CMake to a recent stable version (3.20+).

  2. Compilation (Build) Errors:

    • Issue: Many C++ compilation errors (g++ or clang++ output).
    • Cause: Often related to C++ compiler versions, missing C++ standard library components, or specific flags/features not supported by your compiler.
    • Solution: Verify your C++ compiler (g++ or clang++) is up-to-date. Ensure you’re using a C++17 or C++20 compatible compiler. Review the HEIR build instructions for specific compiler recommendations.

  3. heir-opt Command Not Found or Permission Denied:

    • Issue: ./bin/heir-opt: No such file or directory or Permission denied.
    • Cause: You might not be in the HEIR/build directory, or the build process failed to create the heir-opt executable. If “Permission denied,” the executable might not have execute permissions.
    • Solution: Double-check your current directory. Re-run cmake --build . to ensure compilation completed. If permission denied, try chmod +x ./bin/heir-opt.

  4. Misunderstanding HEIR’s Current State:

    • Issue: Expecting a direct numeric output from heir-opt for a “Hello World” (e.g., input 2 results in 7).
    • Cause: HEIR is a compiler framework in active development. Its current focus is on the intermediate transformations and optimizations, not yet on providing a fully integrated, user-friendly runtime for all FHE backends that produces immediate numeric results.
    • Solution: Adjust expectations. The “Hello World” is about successfully compiling and transforming your FHE program’s representation, demonstrating HEIR’s core functionality as a compiler. Full end-to-end execution will evolve as the project matures and integrates with specific FHE libraries.

Summary

Congratulations! You’ve successfully navigated the initial steps of setting up the HEIR compiler and processed your first conceptual FHE program.

Here are the key takeaways from this chapter:

  • HEIR is a Compiler Framework: It translates high-level FHE computations into lower-level, optimized forms for FHE backends.
  • Active Development: As of 2026-08-18, HEIR is in active development, meaning the focus is on the compilation pipeline, and direct end-to-end execution might require specific helper tools or further integration.
  • Setup Involves CMake: You learned to clone the repository, use CMake for configuration, and build the HEIR tools from source.
  • MLIR as Intermediate Representation: FHE computations are described using an MLIR-like syntax, which HEIR then processes.
  • heir-opt for Transformations: The heir-opt utility allows you to apply various passes to your MLIR code, demonstrating HEIR’s ability to analyze and transform FHE programs.

In the next chapter, we’ll delve deeper into the types of operations HEIR can handle and explore how more complex FHE computations, like those needed for private AI inference, are represented and compiled.

References

This page is AI-assisted and reviewed. It references official documentation and recognized resources where relevant.