A Layered Near-Memory Architecture for Accelerating Multi-Scheme Fully Homomorphic Encryption

Core Concepts

APACHE, a layered near-memory computing hierarchy, is proposed to significantly enhance the utilization rates of both computational resources and memory bandwidth for accelerating multi-scheme fully homomorphic encryption.

Abstract

The paper presents APACHE, a layered near-memory computing architecture designed for general-purpose fully homomorphic encryption (FHE) acceleration. The key insights and contributions are:

Multi-Level Processing-Near-Memory (PNM) Architecture:

APACHE employs a three-level memory hierarchy to fully exploit the internal bandwidth of DIMM units, reducing the external I/O bandwidth requirement by up to 3.15 × 10^5 times.
The computation-light but memory-heavy units are placed closer (or even into) the memory die to minimize data movement.

Configurable Interconnect Topology:

APACHE proposes a configurable interconnect topology with fine-grained functional unit designs to simultaneously ensure multi-scheme operator support and high hardware utilization rates.
The utilization rates of the (I)NTT functional units can be kept at least 90% across multi-scheme FHE tasks, compared to 50% to 85% in existing accelerators.

Operator- and Task-level Scheduling:

APACHE extracts control and data flow of multi-scheme FHE operators to determine the proper resource allocations and datapath configurations.
It also exploits task-level operator parallelism to take advantage of the massive parallel processing capability of multi-channel DIMMs.

The evaluation results show that APACHE achieves 2.4× to 19.8× speedup compared to the state-of-the-art FHE accelerators on a set of multi-scheme FHE operator and application benchmarks.

Stats

The bandwidth demand of CKKS operators is cited from [77] to be up to 8 TB/s.
The size of bootstrapping keys ranges from 37 MB to 1.8 GB across different FHE schemes.

Quotes

"To further enhance its usability, increasing attention focuses on designing protocols that adopt multi-scheme FHE constructions."
"Consequently, the effective acceleration of multi-scheme FHE protocols demands further studies on a unified architecture for the acceleration of both computation-heavy and data-heavy operators across different FHE schemes."

Key Insights Distilled From

APACHE: A Processing-Near-Memory Architecture for Multi-Scheme Fully Homomorphic Encryption

by Lin Ding,Son... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15819.pdf

Deeper Inquiries

How can the proposed APACHE architecture be extended to support other emerging cryptographic primitives beyond FHE, such as secure multi-party computation and differential privacy

The APACHE architecture can be extended to support other emerging cryptographic primitives beyond Fully Homomorphic Encryption (FHE) by incorporating additional functional units and operators tailored to the specific requirements of these primitives. For secure multi-party computation, the architecture can include modules for secure communication protocols, cryptographic key management, and secure computation protocols. This would involve integrating functionalities such as secure data sharing, secure computation, and secure result aggregation.
For differential privacy, the APACHE architecture can be enhanced with modules for data anonymization, noise injection mechanisms, and privacy-preserving data analysis algorithms. This would enable the processing of sensitive data while preserving individual privacy through techniques like differential privacy guarantees and secure data aggregation.

What are the potential challenges and trade-offs in integrating the APACHE architecture with existing CPU-GPU heterogeneous computing platforms

Integrating the APACHE architecture with existing CPU-GPU heterogeneous computing platforms presents both challenges and trade-offs. One potential challenge is the synchronization and coordination of tasks between the APACHE module and the CPU-GPU system. This requires efficient data transfer mechanisms, task scheduling algorithms, and synchronization protocols to ensure seamless operation and optimal performance.
Another challenge is the optimization of resource utilization and workload distribution between the APACHE module and the CPU-GPU system. This involves balancing the computational load, memory access, and data transfer between the different components to maximize overall system efficiency.
Trade-offs may arise in terms of power consumption, latency, and scalability. Integrating the APACHE architecture with CPU-GPU systems may introduce additional power requirements due to the increased computational workload. Latency issues may also arise from data transfer between the APACHE module and the CPU-GPU system. Scalability concerns may arise when scaling the system to handle larger datasets and more complex computations.

Can the layered near-memory computing hierarchy and configurable interconnect topology in APACHE be generalized to accelerate other data-intensive applications beyond cryptography, such as graph analytics and sparse linear algebra

The layered near-memory computing hierarchy and configurable interconnect topology in APACHE can be generalized to accelerate other data-intensive applications beyond cryptography, such as graph analytics and sparse linear algebra. By adapting the architecture to these applications, significant performance improvements can be achieved in terms of computational efficiency and memory bandwidth utilization.
For graph analytics, the configurable interconnect topology can be utilized to optimize graph traversal algorithms, node clustering, and graph processing tasks. The layered near-memory computing hierarchy can enhance the performance of graph algorithms by reducing data movement and improving data locality.
In sparse linear algebra, the architecture can be tailored to efficiently handle sparse matrix operations, matrix factorizations, and iterative solvers. The configurable interconnect topology can be leveraged to optimize memory access patterns and data flow for sparse linear algebra computations. The layered near-memory computing hierarchy can improve the efficiency of sparse matrix-vector multiplication and other sparse linear algebra operations by minimizing data transfer overhead and maximizing computational resources utilization.