  Fully Homomorphic Encryption (FHE), a novel cryptographic theory enabling
computation directly on ciphertext data, offers significant security benefits
but is hampered by substantial performance overhead. In recent years, a series
of accelerator designs have significantly enhanced the performance of FHE
applications, bringing them closer to real-world applicability. However, these
accelerators face challenges related to large on-chip memory and area.
Additionally, FHE algorithms undergo rapid development, rendering the previous
accelerator designs less perfectly adapted to the evolving landscape of
optimized FHE applications. In this paper, we conducted a detailed analysis of
existing applications with the new FHE method, making two key observations: 1)
the bottleneck of FHE applications shifts from NTT to the inner-product
operation, and 2) the optimal {\alpha} of KeySwitch changes with the decrease
in multiplicative level. Based on these observations, we designed an
accelerator named Taiyi, which includes specific hardware for the inner-product
operation and optimizes the NTT and BConv operations through algorithmic
derivation. A comparative evaluation of Taiyi against previous state-of-the-art
designs reveals an average performance improvement of 1.5x and reduces the area
overhead by 15.7%.
