Matrix: Multi-Cipher Structures Dataflow for Parallel and Pipelined TFHE Accelerator

Fully homomorphic encryption over torus (TFHE) enables the execution of arbitrary functions on encrypted data through programmable bootstrapping (PBS). However, performing all operations on ciphertext during PBS results in high computational and memory requirements, limiting the deployment of PBS in real-world scenarios. Previous TFHE accelerator designs have attempted to improve performance by employing specific dataflow and functional units, but these techniques may require large off-chip bandwidth or on-chip storage when scaling up computation capacity. Additionally, the design of specialized functional units may limit the utilization of computation units when facing dynamic secure parameter settings. To address these challenges and further improve PBS throughput in TFHE, we propose Matrix , an ASIC-based architecture that balances off-chip bandwidth and on-chip storage according to the execution flow of PBS. In Matrix , we utilize a unified special-prime-based processing element (PE) that achieves high utilization with minimal resource overhead. Furthermore, we propose a hybrid PBS dataflow that can efficiently reduce computation complexity and memory requirements. Compared to state-of-the-art TFHE accelerators, Matrix achieves 1.43 × -5.66 × throughput improvement for PBS. For ZAMA Deep-NN benchmark, we achieve 525.60 × and 68.06 × speedup compared to CPU and GPU, respectively.