Training layered perceptrons using low accuracy computation

It is demonstrated that the random search approach to training layered perceptrons can be performed using low-accuracy computational precision, and therefore can be implemented using analog computational accuracy. In spite of their numerical stability, random search techniques suffer from ever-increasing search time as dimensionality grows. In response, the authors introduce a modified random search technique, improved bidirectional random optimization (IBRO), to improve the search accuracy per iteration. The proposed scheme should reduce overall search iterations dramatically. The authors compare the performance of IBRO with that of the bidirectional random optimization method through simulations