BMoss: Reconfigurable Hardware Accelerator for Scalable Plagiarism Detection

Accurately and efficiently detecting plagiarism is a critical component of high-quality education. Many plagiarism detection algorithms are being invented and deployed, but these services often come at a significant cost due to their computational overhead. To minimize the cost of plagiarism detection, this work presents BMoss, an efficient reconfigurable hardware accelerator for detecting plagiarism of text or code. It implements the popular MOSS (Measure Of Software Similarity) algorithm, whose performance is typically bottlenecked by repeatedly calculating and comparing hashes of fixed-size substrings. We develop a configurable, pipelined architecture for both has calculation and the Winnowing algorithm, which are the core components of MOSS. We evaluate BMoss against multiple software implementations of MOSS, including our own optimized software implementation which uses a fast xxHash function to outperform all open implementations. We show that using an affordable FPGA-based Alveo U50 accelerator device, we improve the performance and power efficiency of MOSS by over an order of magnitude compared to single-thread implementations. Compared to a much costlier Xeon server, BMoss achieves almost 2× performance and over 3× power efficiency.