  Homomorphic encryption (HE) enables computations on encrypted data by
concealing information under noise for security. However, the process of
bootstrapping, which resets the noise level in the ciphertext, is
computationally expensive and requires a large bootstrapping key. The TFHE
scheme offers a faster and programmable bootstrapping algorithm called PBS,
crucial for security-focused applications like machine learning. Nevertheless,
the current TFHE scheme lacks support for ciphertext packing, resulting in low
throughput. This work thoroughly analyzes TFHE bootstrapping, identifies the
bottleneck in GPUs caused by the blind rotation fragmentation problem, and
proposes a hardware TFHE accelerator called Strix. Strix introduces a two-level
batching approach to enhance the batch size in PBS, utilizes a specialized
microarchitecture for efficient streaming data processing, and incorporates a
fully-pipelined FFT microarchitecture to improve performance. It achieves
significantly higher throughput than state-of-the-art implementations on both
CPUs and GPUs, outperforming existing TFHE accelerators by a factor of 7.4.
