Howard C. Card

Cellular automata-based pseudorandom number generators for built-in self-test

A variation on a built-in self-test technique is presented that is based on a distributed pseudorandom number generator derived from a one-dimensional cellular automata (CA) array. The cellular automata-logic-block-observation circuits presented are expected to improve upon conventional design for testability circuitry such as built-in logic-block operation as a direct consequence of reduced cross correlation between the bit streams that are used as inputs to the logic unit under test. Certain types of circuit faults are undetectable using the correlated bit streams produced by a conventional linear-feedback-shift-register (LFSR). It is also noted that CA implementations exhibit data compression properties similar to those of the LFSR and that they display locality and topological regularity, which are important attributes for a very large-scale integration implementation. It is noted that some CAs may be able to generate weighted pseudorandom test patterns. It is also possible that some of the analysis of pseudorandom testing may be more directly applicable to CA-based pseudorandom testing than to LFSR-based schemes. >

Stochastic neural computation. I. Computational elements

This paper examines a number of stochastic computational elements employed in artificial neural networks, several of which are introduced for the first time, together with an analysis of their operation. We briefly include multiplication, squaring, addition, subtraction, and division circuits in both unipolar and bipolar formats, the principles of which are well-known, at least for unipolar signals. We have introduced several modifications to improve the speed of the division operation. The primary contribution of this paper, however, is in introducing several state machine-based computational elements for performing sigmoid nonlinearity mappings, linear gain, and exponentiation functions. We also describe an efficient method for the generation of, and conversion between, stochastic and deterministic binary signals. The validity of the present approach is demonstrated in a companion paper through a sample application, the recognition of noisy optical characters using soft competitive learning. Network generalization capabilities of the stochastic network maintain a squared error within 10 percent of that of a floating-point implementation for a wide range of noise levels. While the accuracy of stochastic computation may not compare favorably with more conventional binary radix-based computation, the low circuit area, power, and speed characteristics may, in certain situations, make them attractive for VLSI implementation of artificial neural networks.

Parallel random number generation for VLSI systems using cellular automata

A novel random number generation (RNG) architecture of particular importance in VLSI for fine-grained parallel processing is proposed. It is demonstrated that efficient parallel pseudorandom sequence generation can be accomplished using certain elementary one-dimensional cellular automata (two binary states per site and only nearest-neighbor connections). The pseudorandom numbers appear in parallel from various cells in the cellular automaton on each clock cycle and pass standard empirical random number tests. Applications have been demonstrated in the design and analysis of special-purpose accelerators for Monte Carlo simulation of large intractable systems. In addition, significant advantages in pseudorandom built-in self-test of VLSI circuits using cellular automata based RNGs have been demonstrated. >

Birdsong recognition using backpropagation and multivariate statistics

An investigation has been made of bird species recognition using recordings of birdsong. Six species of birds native to Manitoba were chosen: song sparrows, fox sparrows, marsh wrens, sedge wrens, yellow warblers, and red-winged blackbirds. These species exhibit overlapping characteristics in terms of frequency content, song components, and length of songs. Songs from multiple individuals in each of these species were employed, with discernible recording noise such as tape hiss and, in some cases, other competing songs in the background. These songs were analyzed using backpropagation learning in two-layer perceptrons, as well as methods from multivariate statistics that included principal components and quadratic discriminant analysis. Preprocessing methods included linear predictive coding and windowed Fourier transforms. Generalization performance ranged from 82-93 % correct identification, with the lower figures corresponding to smaller networks employing more preprocessing for dimensionality reduction. At the same time, the computational requirements were significantly reduced in this case.

Tolerance to analog hardware of on-chip learning in backpropagation networks

In this paper we present results of simulations performed assuming both forward and backward computation are done on-chip using analog components. Aspects of analog hardware studied are component variability, limited voltage ranges, components (multipliers) that only approximate the computations in the backpropagation algorithm, and capacitive weight decay. It is shown that backpropagation networks can learn to compensate for all these shortcomings of analog circuits except for zero offsets, and the latter are correctable with minor circuit complications. Variability in multiplier gains is not a problem, and learning is still possible despite limited voltage ranges and function approximations. Fixed component variation from fabrication is shown to be less detrimental to learning than component variation due to noise. Weight decay is tolerable provided it is sufficiently small, which implies frequent refreshing by rehearsal on the training data or modest cooling of the circuits. The former approach allows for learning nonstationary problem sets.

Vector quantization of images using modified adaptive resonance algorithm for hierarchical clustering

A modified adaptive resonance theory (ART2) learning algorithm, which we employ in this paper, belongs to the family of NN algorithms whose main goal is the discovery of input data clusters, without considering their actual size. This feature makes the modified ART2 algorithm very convenient for image compression tasks, particularly when dealing with images with large background areas containing few details. Moreover, due to the ability to produce hierarchical quantization (clustering), the modified ART2 algorithm is proved to significantly reduce the computation time required for coding, and therefore enhance the overall compression process. Examples of the results obtained are presented, suggesting the benefits of using this algorithm for the purpose of VQ, i.e., image compression, over the other NN learning algorithms.

Stochastic neural computation. II. Soft competitive learning

For pt. I see ibid., p.891-905. An investigation has been made into the use of stochastic arithmetic to implement an artificial neural network solution to a typical pattern recognition application. Optical character recognition is performed on very noisy characters in the E-13B MICR font. The artificial neural network is composed of two layers, the first layer being a set of soft competitive learning subnetworks and the second a set of fully connected linear output neurons. The observed number of clock cycles in the stochastic case represents an order of magnitude improvement over the floating-point implementation assuming clock frequency parity. Network generalization capabilities were also compared based on the network squared error as a function of the amount of noise added to the input patterns. The stochastic network maintains a squared error within 10 percent of that of the floating-point implementation for a wide range of noise levels.

Cellular automata-based signature analysis for built-in self-test

Relevant signature analysis properties for elementary one-dimensional cellular automata are presented. It is found that cellular automata with cyclic-group rules provide signature analysis properties comparable to the LFSR (linear feedback shift register). A technique of using CALBO (cellular automata-based logic block observation) for both test pattern generation and signature analysis, in a similar manner to a typical BILBO (built-in block observation) implementation, is presented. >

Competitive learning algorithms and neurocomputer architecture

This paper begins with an overview of several competitive learning algorithms in artificial neural networks, including self-organizing feature maps, focusing on properties of these algorithms important to hardware implementations. We then discuss previously reported digital implementations of these networks. Finally, we report a reconfigurable parallel neurocomputer architecture we have designed using digital signal processing chips and field-programmable gate array devices. Communications are based upon a broadcast network with FPGA-based message preprocessing and postprocessing. A small prototype of this system has been constructed and applied to competitive learning in self-organizing maps. This machine is able to model slowly-varying nonstationary data in real time.

A review of amorphous silicon alloys: Fabrication, properties and applications

A review of amorphous silicon alloys (other than a-Si: H) is presented. The main focus is on experimental results. Methods of fabricating amorphous alloys are classified and their basic operational principles outlined. The electrical and optical properties of amorphous silicon based alloys are then described, and a summary of existing and potential applications given. Conspicuous gaps in the fabrication, understanding and application of these materials are pointed out. A comprehensive (though not exhaustive) bibliography is presented, with references to all amorphous silicon alloys studied up to the summer of 1986.