Intel’s Heracles Chip Accelerates Fully Homomorphic Encryption for AI

Concerns about data privacy are driving innovation in how artificial intelligence processes sensitive information. Fully Homomorphic Encryption (FHE) offers a solution – the ability to perform computations on encrypted data without decrypting it. However, the computational cost has historically been prohibitive. Intel’s recent demonstration of its Heracles chip represents a significant step toward making FHE practical for real-world AI applications.

The Challenge of Computing on Encrypted Data

FHE allows for data to remain encrypted during processing, addressing privacy concerns in cloud computing, data analytics, and collaborative environments. Unlike traditional encryption, which only protects data at rest or in transit, FHE secures data while it’s being used. Duality Tech explains that the output of a computation performed with FHE is also encrypted, requiring decryption only by the data owner.

However, a major hurdle has been the immense computational overhead. Performing calculations on encrypted data can take thousands of times longer than on plaintext data. This limitation has hindered the widespread adoption of FHE, despite its potential benefits.

Intel’s Heracles: A Hardware Accelerator for FHE

Intel has developed Heracles, a specialized chip designed to accelerate FHE computations. Demonstrated at the IEEE International Solid-State Circuits Conference (ISSCC) in San Francisco, Heracles achieved speedups of up to 5,000-fold compared to a top-of-the-line Intel server CPU. IBM Research highlights the potential of FHE to secure cloud workloads and enable AI applications to process sensitive data without the risk of data breaches.

Heracles distinguishes itself through its scale and advanced technology. At approximately 20 times larger than other FHE research chips (around 10 square millimeters), it’s built using Intel’s 3-nanometer FinFET technology. The chip is paired with two 24-gigabyte high-bandwidth memory chips, a configuration typically found in GPUs used for AI training.

Demonstrating Real-World Performance

Intel demonstrated Heracles’ capabilities with a simulated voter verification scenario. A voter encrypts their ID and vote and sends it to an encrypted database. The system, without decrypting the data, determines if the ballot is valid and returns an encrypted response.

This process, which took 15 milliseconds on an Intel Xeon server CPU, was completed in just 14 microseconds by Heracles. Extrapolating this performance, verifying 100 million voter ballots would take over 17 days on a CPU but only 23 minutes on Heracles.

The Architecture Behind Heracles

Heracles features 64 compute cores, arranged in an eight-by-eight grid, designed for parallel processing of the complex mathematical operations inherent in FHE. An on-chip mesh network facilitates high-speed data transfer between the cores. The chip also incorporates 48 GB of high-bandwidth memory with 819 GB per second connections and 64 MB of cache memory, enabling efficient data flow.

To optimize performance, Heracles employs three synchronized instruction streams: one for data input/output, one for internal data movement, and one for mathematical computations.

Competition and Future Directions

While Intel’s Heracles represents a significant advancement, other companies are also pursuing FHE acceleration. NYU researchers have introduced Orion, a framework for FHE-based deep learning, demonstrating high-resolution object detection on encrypted data. Startups like Niobium Microsystems, Fabric Cryptography, and Optalysys are also developing FHE accelerator chips.

Intel plans to continue refining Heracles, focusing on software optimization and exploring hardware improvements for future generations. The company views this as the beginning of a long journey toward widespread adoption of FHE.

Key Takeaways

• Fully Homomorphic Encryption (FHE) enables computation on encrypted data, preserving privacy.
• Intel’s Heracles chip significantly accelerates FHE computations, achieving up to 5,000x speedups compared to traditional CPUs.
• Hardware acceleration is crucial for making FHE practical for large-scale AI applications.
• Competition in the FHE hardware space is growing, with startups and research institutions developing innovative solutions.