  The amount of data in real-time, such as time series and streaming data,
available today continues to grow. Being able to analyze this data the moment
it arrives can bring an immense added value. However, it also requires a lot of
computational effort and new acceleration techniques. As a possible solution to
this problem, this paper proposes a hardware architecture for Typicality and
Eccentricity Data Analytic (TEDA) algorithm implemented on Field Programmable
Gate Arrays (FPGA) for use in data streaming anomaly detection. TEDA is based
on a new approach to outlier detection in the data stream context. In order to
validate the proposals, results of the occupation and throughput of the
proposed hardware are presented. Besides, the bit accurate simulation results
are also presented. The project aims to Xilinx Virtex-6 xc6vlx240t-1ff1156 as
the target FPGA.
