String Matching on Multicontext FPGAs using Self-Reconfiguration

FPGAs can perform better than ASICs if the logic mapped onto them is optimized for each problem instance. Unfortunately, this advantage is often canceled by the long time needed by CAD tools to generate problem instance dependent logic and the time required to configure the FPGAs. In this paper, a novel approach for runtime mapping is proposed that utilizes self-reconfigurability of multicontext FPGAs to achieve very high speedups over existing approaches. The key idea is to design and map logic onto a multicontext FPGA that in turn maps problem instance dependent logic onto other contexts of the same FPGA. As a result, CAD tools need to be used just once for each problem and not once for every problem instance as is usually done. To demonstrate the feasibility of our approach, a detailed implementation of the KMP string matching algorithm is presented which involves runtime construction of a finite state machine. We implement the KMP algorithm on a conventional FPGA (Xilinx XC 6216) and use it to obtain accurate estimates of performance on a multicontext device. Speedups in mapping time of 10 over CAD tools and more than 1800 over a program written specifically for FSM generation were obtained. Significant speedups were obtained in overall execution time as well, including a speedup ranging from 3 to 16 times over a software implementation of the KMP algorithm running on a Sun Ultra 1 Model 140 workstation.