FPGAs Accelerate Transformer AI for Real-Time Anomaly Detection

This study investigates the optimization of Transformer neural networks for real-time outlier detection, specifically targeting financial time series data. Financial data often contains errors or anomalies that can compromise downstream processing, and the sheer volume of this data necessitates highly efficient cleaning methods. Transformers, known for their ability to capture long-range dependencies, are well-suited for time series modeling and anomaly detection. The research focuses on leveraging Field-Programmable Gate Arrays (FPGAs) to accelerate the inference time of these Transformer architectures. FPGAs offer reconfigurability and high performance, making them ideal for speeding up complex computations. The authors explore various Transformer architectures and their efficient implementation on an FPGA board (PYNQ-Z2). The goal is to minimize latency for anomaly detection, demonstrating how hardware acceleration can significantly enhance the practical application of advanced AI models in demanding real-time scenarios.