Key and recollection vector effects on heteroassociative memory performance

A semi-holographic hyperdimensional representation system for hardware-friendly cognitive computing

One of the main, long-term objectives of artificial intelligence is the creation of thinking machines. To that end, substantial effort has been placed into designing cognitive systems; i.e. systems that can manipulate semantic-level information. A substantial part of that effort is oriented towards designing the mathematical machinery underlying cognition in a way that is very efficiently implementable in hardware. In this work, we propose a 'semi-holographic' representation system that can be implemented in hardware using only multiplexing and addition operations, thus avoiding the need for expensive multiplication. The resulting architecture can be readily constructed by recycling standard microprocessor elements and is capable of performing two key mathematical operations frequently used in cognition, superposition and binding, within a budget of below 6 pJ for 64-bit operands. Our proposed 'cognitive processing unit' is intended as just one (albeit crucial) part of much larger cognitive systems where artificial neural networks of all kinds and associative memories work in concord to give rise to intelligence. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.

Class-associative multiple target detection by use of fringe-adjusted joint transform correlation

We propose a class-associative correlation filter based technique for detecting a class of objects consisting of dissimilar patterns. The fringe-adjusted joint transform correlation algorithm is utilized to enhance the correlation performance, thus ensuring a strong and equal correlation peak for each element of the selected class. For enhanced performance, an enhanced version of the fringe-adjusted filter is incorporated in the class-associative multiple target detection process. The feasibility of the proposed technique has been tested by computer simulation.

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.

Spatial light rebroadcaster bit-slice word-addressable holographic memory

A heteroassociative memory is proposed that allows a key word in a dictionary of key words to be used to recall an associated holographic image in a database of images. A novel bit-slice search approach finds the word sought in a dictionary of key words and generates a beam that selects the corresponding holographic image from a directory of images. Spatial light rebroadcasters are proposed for the key-word database. Optical experimental results demonstrate that spatial light rebroadcasters may be used in loops with image intensifiers and optically addressable liquid-crystal light valves and that the approach can be used to select holograms based on key-word matching. In the case considered, a holographic image having more than 40,000 bits is selected from 8 by using a key word from a dictionary of 8 words.

Optical heteroassociative memory using spatial light rebroadcasters

A new device, a spatial light rebroadcaster, is described that can have optical disk resolution and high speed. Experimental results demonstrate resolution, speed, linearity, logic operation, and arithmetic computation. The device is suitable for optical computing, in particular for memory systems. Optical masking for controlling memory recall and 100 x 100 matrix-vector multiplication are demonstrated. A one pass optical heteroassociative memory system was assembled that uses an optical outer product formulation to store associated 32-bit vectors. Recall is achieved by optical matrix-vector multiplication. The results show the suitability of these devices for memory systems in optical computers.

Ho-Kashyap optical associative processors

A Ho-Kashyap (H-K) associative processor (AP) is shown to have a larger storage capacity than the pseudoinverse and correlation APs and to accurately store linearly dependent key vectors. Prior APs have not demonstrated good performance on linearly dependent key vectors. The AP is attractive for optical implementation. A new robust H-K AP is proposed to improve noise performance. These results are demonstrated both theoretically and by Monte Carlo simulation. The H-K AP is also shown to outperform the pseudoinverse AP in an aircraft recognition case study. A technique is developed to indicate the least reliable output vector elements and a new AP error correcting synthesis technique is advanced.