Part 3: ASICs | Hardware Acceleration of FHE

Custom hardware has always played a crucial role in optimizing computational workloads. Whether it’s GPUs accelerating deep learning or FPGAs boosting real-time data processing, specialized chips are the first-considered solutions for efficiency. But when it comes to Fully Homomorphic Encryption (FHE), things get even more interesting. The computational overhead of FHE is quite high, putting excess strain on general-purpose hardware. This is where Application-Specific Integrated Circuits (ASICs) come into play.

Let’s see what ASICs are, how they work, and why they may be a game-changer for FHE.

What Is an ASIC?

An Application-Specific Integrated Circuit (ASIC) is exactly what it sounds like: a chip designed for a specific task. Unlike general-purpose processors (CPUs) that handle a wide range of operations or GPUs that optimize parallel tasks, ASICs are built to execute a predefined function as efficiently as possible.

ASICs are everywhere. They power Bitcoin mining, optimize networking hardware, and drive AI inferencing. Their advantage lies in their ability to execute dedicated tasks with high speed and energy efficiency, outperforming general-purpose alternatives.

Why FHE Needs Acceleration, Revisited

• Fully Homomorphic Encryption (FHE) allows computations on encrypted data without decryption, preserving privacy. But this security comes at a cost: FHE operations require intensive mathematical computations, including polynomial multiplications and bootstrapping. These tasks are slow and power-hungry when executed on standard processors.

This is where ASICs can be useful. By utilizing these specialized hardwares to perform FHE-specific tasks, we can achieve significant performance boosts and make practical FHE applications feasible.

How ASICs Accelerate FHE

FHE computations rely heavily on lattice-based cryptography, polynomial arithmetic, and noise management. ASICs can optimize these areas by focusing on three key components:

1. Polynomial Multiplication

Most FHE schemes rely on operations over polynomial rings, which involve computationally expensive multiplications. ASICs can implement fast modular multiplication circuits optimized for the specific polynomial sizes used in FHE, reducing latency and power consumption.

For the technical aspects of different FHE schemes, take a look at our previous posts.

2. Bootstrapping Optimization

Bootstrapping is the process of refreshing ciphertexts to maintain decryption accuracy. This step is notoriously slow. ASICs can integrate dedicated circuits for programmable bootstrapping, significantly reducing computation time. Some designs use specialized number-theoretic transform (NTT) hardware to speed up polynomial evaluations.

If you’re curious about the bootstrapping process from a technical perspective, see TFHE Scheme 2 | Building Blocks of FHE.

3. Memory and Parallel Processing

FHE computations generate large intermediate values that must be stored and accessed efficiently. ASICs can include high-speed memory buffers and parallel execution units to handle these workloads effectively, ensuring minimal data transfer issues.

Comparing ASICs to GPUs and FPGAs for FHE

While GPUs and FPGAs have been used to accelerate FHE, they have limitations:

• GPUs are good at parallelism but are not optimized for modular arithmetic, which is a core component of FHE.
• FPGAs offer flexibility but lack the raw computational power of an ASIC designed specifically for FHE tasks.
• ASICs have some advantages both in efficiency by eliminating unnecessary overhead and focusing purely on FHE-related computations.

The Future of FHE Acceleration with ASICs

As the demand for privacy-preserving computation grows, ASICs will play a critical role in making FHE practical for real-world applications for sure. From confidential smart contracts to secure cloud computing, hardware acceleration is the key to unlocking FHE’s full potential and the future is hopefully bright.

Efforts are already underway to design ASICs specifically for FHE, with research focusing on optimizing energy efficiency, reducing bootstrapping costs, and improving overall throughput. As these advancements continue, we can expect FHE to become significantly faster, opening up new possibilities in secure computation.

Conclusion

ASICs are one of the paths to scaling FHE. By leveraging hardware to the specific needs of FHE computations, we can overcome its performance bottlenecks and unlock a future where encrypted data can be computed efficiently. The intersection of cryptography and hardware engineering is where the next big breakthroughs in privacy will happen.

For more on FHE and cryptographic acceleration, keep an eye on our blog.