In the domain of password recovery, deep learning has emerged as a pivotal technology for enhancing recovery efficiency. Despite its effectiveness, the inherent computation complexity of deep learning-based password generation algorithms poses substantial challenges, particularly in achieving synergistic acceleration between deep learning inference, and plaintext encryption process. In this paper, we introduce PassRecover, a multi-FPGA-based computing system that can simultaneously accelerate deep learning-driven password generation and plaintext encryption in an end-to-end manner. The system architecture incorporates a neural processing unit (NPU) and an encryption array configured to operate under a streaming dataflow paradigm for parallel processing. It is the first approach to explore the benefit of end-to-end offline password recovery. For comprehensive evaluation, PassRecover is benchmarked against PassGAN and five industry-standard encryption algorithms (Office2010, Office2013, PDF1.7, Winzip, and RAR5). Experimental results demonstrate excellent performance: Compared to the latest work that only accelerate encryption algorithms, PassRecover achieves an average 101.5% speedup across all tested encryption algorithms. When compared to graphics processing unit (GPU)-based end-to-end implementations, this work delivers 93.01% faster processing speeds and $3.73\times$ superior energy efficiency. These results establish PassRecover as a promising solution for resource-constrained password recovery scenarios requiring high throughput and energy efficiency.