A gate-level EHW chip: Implementing GA operations and reconfigurable hardware on a single LSI

Abstract

The advantage of Evolvable Hardware (EHW) over traditional hardware is its capacity for dynamic and autonomous adaptation, which is achieved through by Genetic Algorithms (GAs). In most EHW implementations, these GAs are executed by software on a personal computer (PC) or workstation (WS). However, as a wider variety of applications come to utilize EHW, this is not always practical. One solution is to have the GA operations carried out by the hardware itself, by integrating these together with reconfigurable hardware logic like PLA (Programmble Logic Array) or FPGA (Field Programmable Gate Array) on to a single LSI chip. A compact and quickly reconfigurable EHW chip like this could service as an off-the-shelf device for practical applications that require on-line hardware reconfiguration. In this paper, we describe an integrated EHW LSI chip that consists of GA hardware, reconfigurable hardware logic, a chromosome memory, a training data memory, and a 16-bit CPU core (NEC V30). An application of this chip is also described in a myoelectric artificial hand, which is operated by muscular control signals. Although, work on using neural networks for this is being carried out, this approach is not very promising due to the long learning period required for neural networks. A simulation is presented showing that not only is the EHW performance slightly better than with neural networks, but that the learning time is considerably reduced.KeywordsInput PatternUniform CrossoverMyoelectric SignalReconfigurable HardwareGenetic Algorithm ProgramThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.