UC Riverside

With the increase in available data, specifically time series data, the importance of different data analysis techniques has increased. One technique used by many data scientists is finding characteristics within subsequences of the data set. Characteristics, or features can be quantified by a process known as feature extraction . This feature extraction step is often computationally expensive, and usually requires the availability of the entire data set. During the data collection stage, data scientists may want to see patterns or characteristics. This can be achieved with Real-Time Feature Extraction, by computing feature sets while data streams in from a source. FPGAs, or Field Programmable Gate Arrays, have access to a plethora of I/O that allows for data to be streamed directly into the computational units making for efficient,real-time feature extraction. In this paper, we provide an FPGA architecture that is able to extract features in real-time, while offering latency, and power optimizations.