“Next-generation DNA sequencing technology” has developed biomedical research, making genome and Protein sequencing an affordable and frequently used tool for a wide variety of research applications such as DNA Searching , DNA Sequencing, Drug Discovery, etc., objective of this work is to propose space and power efficient hardware architecture for micro sequence identification. Bloom filter, PCR, SRA, DFT and Phylogeny Aware are the recent related works for micro sequence identification. We propose an FPGA based multilevel sequence similarity identification to reduce the computational overhead, time and data complexity, GENIE, UNI, dbGap are the benchmarked database considered for the validation of the proposed method. Micro sequence identification uses a pattern mining technique. First this method generates number of patterns or sequences from the dimension 2 to the dimension N. The patterns are generated at each dimension and with varying size of dimension. The generated patterns are stored in the pattern set and for the input sequence which generates the similar set of pattern set. Based on generated pattern set, the proposed method computes the sequence similarity at each level and finally a cumulative similarity value is computed. Performance of the proposed method is compared with existing Bloom filter, PSR, SRA and DFT.