Fully Homomorphic Encryption (FHE) is a set of powerful cryptographic schemes
that allows computation to be performed directly on encrypted data with an
unlimited depth. Despite FHE’s promising in privacy-preserving computing, yet
in most FHE schemes, ciphertext generally blows up thousands of times compared
to the original message, and the massive amount of data load from off-chip
memory for bootstrapping and privacy-preserving machine learning applications
(such as HELR, ResNet-20), both degrade the performance of FHE-based
computation. Several hardware designs have been proposed to address this issue,
however, most of them require enormous resources and power. An acceleration
platform with easy programmability, high efficiency, and low overhead is a
prerequisite for practical application.
This paper proposes EFFACT, a highly efficient full-stack FHE acceleration
platform with a compiler that provides comprehensive optimizations and
vector-friendly hardware. We start by examining the computational overhead
across different real-world benchmarks to highlight the potential benefits of
reallocating computing resources for efficiency enhancement. Then we make a
design space exploration to find an optimal SRAM size with high utilization and
low cost. On the other hand, EFFACT features a novel optimization named
streaming memory access which is proposed to enable high throughput with
limited SRAMs. Regarding the software-side optimization, we also propose a
circuit-level function unit reuse scheme, to substantially reduce the computing
resources without performance degradation. Moreover, we design novel NTT and
automorphism units that are suitable for a cost-sensitive and highly efficient
architecture, leading to low area. For generality, EFFACT is also equipped with
an ISA and a compiler backend that can support several FHE schemes like CKKS,
BGV, and BFV.

Cet article explore les excursions dans le temps et leurs implications.

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