The paper proposes a new technology for intelligent machines based on the concept of programmable hardware. To build an intelligent system, the designer has to adapt it to the problem. First we show that programmable hardware is an intermediate step for building configurations, in order to choose the best architecture. In this case, the tasks are performed in a time period that respects human cognitive capacities. Next is detailed a multi level model composed of the cognitive, software and hardware levels. An experimental platform has been built, based on programmable hardware, and used in a "Grand Challenge" problem: knowledge discovery in genetic sequence databases, to compare the relative efficiencies of programmable hardware and classical Von Neumann based architecture. Programmable hardware is shown to have a significantly faster response time, which is essential for modern day intelligent machine user interaction.

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.

The authors explore the scope of possibilities of the implementation of molecular biology algorithms on a reconfigurable hardware based architecture. In order to demonstrate both the flexibility and power of reconfigurable hardware, two algorithms have been implemented on an architecture constituted by 23 FPGAs (Xilinx XC3090) and 4MB of SRAM. These two algorithms have been chosen because of their biological relevance. One is a classical dynamic programming algorithm that is implemented as a monodimensional systolic array. The other, as described in this paper, is based on pattern detection in long sequences of letters. This algorithm has been implemented as a rapid content addressable memory. It is important to stress the fact that these performances are achieved thanks to the use of the entire possibilities of reconfigurability. This enables one to exploit to the full each level of parallelism of the algorithm. The concept of reconfigurable hardware perfectly suits the needs of molecular biology. The performances demonstrated in this paper are several magnitude orders greater than a 40 MIPS workstation.>