Abstract-Advances in life sciences over the last few decades have lead to the generation of a huge amount of biological data. Computing research has become a vital part in driving biological discovery where analysis and categorization of biological data are involved. String matching algorithms can be applied for protein/gene sequence matching and with the phenomenal increase in the size of string databases to be analyzed, software implementations of these algorithms seems to have hit a hard limit and hardware acceleration is increasingly being sought. Several hardware platforms such as Field Programmable Gate Arrays (FPGA), Graphics Processing Units (GPU) and Chip Multi Processors (CMP) are being explored as hardware platforms. In this paper, we take an FPGA hardware exploration and expedite the design time by a design automation technique. Further, our design automation is also optimized for better hardware utilization through optimizing the number of peptides that can be represented in an FPGA tile. The results indicate significant improvements in design time and hardware utilization which are reported in this paper.

Statechart formalism has been a preferred choice for modeling complex reactive systems (CRS) in recent years. It has inbuilt powerful features of orthogonality, hierarchy, intermodular communication and history. Once statechart based system modeling is done the next issues to be addressed are (1) modular verification of the system for failsafe operation under all possible working conditions (2) progressive controller implementation together with the supervisory control while maintaining traceability and re-configurability and (3) facilitation of controller adaptation for progressive incorporation of security features and supervisory specifications.An elevator system was designed and built to reflect exigencies of a typical CRS hardware/software platform. A controller was designed to meet the above requirements and tested on the platform to validate the feasibility of model-based control design/verification methodology for real scale systems. Modularity was achieved by developing the statechart model of the plant into a tree of communicating language generators. Progresively verified modules were then translated into sequential function charts (SFC) which were finally integrated to form a complete flat SFC. The SFC was then implemented on a PLC platform (Telemechanique). The program was first validated in simulation using Telemechanique "Twidosuite" for different operating conditions and finally tested on the elevator system.

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Pattern matching is commonly required in many application areas and bioinformatics is a major area of interestthat requires both exact and approximate pattern matching.Much work has been done in this area, yet there is still a significant space for improvement in efficiency, flexibility, and throughput.This paper presents a hardware-software co-design of Aho-Corasick algorithm in NiosII soft processor and its scalability for a pattern matching application. A software only approach has used to compare the scalability of hardwaresoftware co-design and according to the results we could conclude that software-hardware co-design implementation could give a maximum of 10 times speed up for automata created with 1200 peptides compared to software-only implementation.Index Terms-Aho-Corasick, Nios II, Hardware-Software co-design I.

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