Ho-Kashyap optical associative processors

A combined evolution method for associative memory networks

In the study of associative memory networks, the updating rule remains unchanged during neuron evolution. When evolution stops at an undesired minimum, simulated annealing is used to resolve the problem. In this paper, a combined neuron evolution method based on a multipath network architecture is presented. It is shown that, by controlling the evolution path, the probability that evolution terminates in undesired, minima is significantly reduced. Once evolution is trapped in an undesired minimum with respect to one path, the method seeks an alternative path to carry on the evolution. The process continues until a desired minimum is reached. Visual examples are used to demonstrate the performance of the method.

Learning and recall algorithm for optical associative memory using a bistable spatial light modulator

A learning and recall algorithm for optical associative memory based on the conventional correlationlearning method with three effective improvements (sparse-encoding method, constant-total-activity method, and binary memory) is proposed from a viewpoint of practical implementation. It is shown that the algorithm is suitable for implementation with a bistable spatial light modulator such as a ferroelectric liquid-crystal spatial light modulator, which has high resolution and a fast response time. The results of theoretical analysis and simulations indicate that the algorithm permits an associative-memory system with a large memory capacity to be realized. An example of an optical system for executing this algorithm is proposed. To determine the performance specifications that are required for the various optical components within the system, we simulate and evaluate the effect of noise (which is caused by nonideal components) on system performance. These results show that the system is robust in the presence of predicted noise levels.

High-capacity neural networks on nonideal hardware

We present a new training-out algorithm for neural networks that permits good performance on nonideal hardware with limited analog neuron and weight accuracy. Optical neural networks are emphasized with the error sources including nonuniform beam illumination and nonlinear device characteristics. We compensate for processor nonidealities during gated learning (off-line training); thus our algorithm does not require real-time neural networks with adaptive weights. This permits use of high-accuracy nonadaptive weights and reduced hardware complexity. The specific neural network we consider is the Ho-Kashyap associative processor because it provides the largest storage capacity. Simulation results and optical laboratory data are provided. The storage measure we use is the ratio M/N of the number of vectors stored (M) to the dimensionality of the vectors stored (N). We show a storage capacity of M/N = 1.5 on our optical laboratory system with excellent recall accuracy, > 95%. The theoretical maximum storage is M/N = 2 (as N approaches infinity), and thus the storage and performance we demonstrate are impressive considering the processor nonidealities we present. Our techniques can be applied to other neural network algorithms and other nonideal processing hardware.

Neural networks applied to diffraction-pattern sampling

While diffraction-pattern sampling has been widely applied in the classification of patterns, still its usage has been limited somewhat by the need to devise rather sophisticated algorithms. In this paper we describe sorting or classification of a variety of patterns with commercially available neural-network software together with the ring-wedge photodetector to supply optical transform data for the input neurons. With this combination of neural networks and diffraction-pattern sampling it is no longer necessary to write specialized software. The training and testing methodology is carried out for this new system, and excellent results are obtained for sorting thumbprints. In sorting thumbprints the neural network can be trained for orientation-independent or wide-scale size-independent classifications by use of ring-only or wedge-only input neurons, respectively. Separate experiments are described for the sorting of particulates. Again, these are cases in which writing appropriate software based on diffraction theory would be extremely difficult. Two interesting novel neural networks are obtained: one is for real-time control of a submicrometer colloidal suspension of CdS, and the second is for concentration measurements of 2.02-µm polyvinyltoulene spheres in methyl alcohol. Widespread new applications are predicted for this hybrid system that combines diffraction-pattern sampling and the neural network.

Symbolic substitution modified signed-digit optical adder

A high-accuracy fixed-point optical adder that operates in parallel on many long words and that uses a pipelined correlator architecture is described. A symbolic substitution algorithm with the modified signed-digit number representation is used to perform fixed-point additions with limited carries. A new set of substitution rules and encodings is developed to combine the recognition and substitution steps into one correlation operation. This reduces hardware requirements, improves throughput by reducing the space-bandwidth product needed, and reduces latency (the delay between when data enter the processor and when the final output is available) by a factor of 2. This algorithm and our new modified signed-digit encodings and substitution rules improve the performance of other correlator and noncorrelator optical numeric computing architectures.

Incoherent optical associative memory by using synthetic discriminant function filters

A new associative memory system applying incoherent correlators is presented. With this system, which is composed of a nonlinear processor and optical-electronic feedback loop, memorized patterns can be recalled without cross talk. To realize a high association ability and stable operation, a synthetic discriminant function filter is applied. Results of the computer simulation and actual experiments by using the system that is constructed here are demonstrated.