Run time reconfiguration of FPGA for scanning genomic databases
This paper evaluates the feasibility of reconfiguring an FPGA at run time, and tests its performance using a "Grand Challenge Problem", the high speed scanning of genomic sequence databases. Algorithm implementation into a XC3090 FPGA is described, and methods proposed for generating a placed Xilinx Netlist File that can be efficiently routed at run time by the Automated Placing and Routing Xilinx tools, in order to increase the speed and the density of the design. The same algorithm carefully optimised on a RISC processor has been compared with the run time reconfigurated FPGA, and shows the latter to have an improvement in speed of two to three orders of magnitude.
Summary form only given, as follows. A study is presented of precision constraints imposed by a hybrid chip architecture with analog neurons and digital backpropagation calculations. Conversions between the analog and digital domains and weight storage restrictions impose precision limits on both analog and digital calculations. It is shown through simulations that a learning system of this nature can be implemented in spite of limited resolution in the analog circuits and using fixed-point arithmetic to implement the backpropagation algorithm. >