Low Voltage Floating Gate MOS Transistor Based Four-Quadrant Multiplier

This paper presents a four-quadrant multiplier based on square-law characteristic of floating gate MOSFET (FGMOS) in saturation region. The proposed circuit uses square-difference identity and the differential voltage squarer proposed by Gupta et al. to implement the multiplication function. The proposed multiplier employs eight FGMOS transistors and two resistors only. The FGMOS implementation of the multiplier allows low voltage operation, reduced power consumption and minimum transistor count. The second order effects caused due to mobility degradation, component mismatch and temperature variations are discussed. Performance of the proposed circuit is verified at ±0.75 V in TSMC 0.18 µm CMOS, BSIM3 and Level 49 technology by using Cadence Spectre simulator

A VLSI-design of the minimum entropy neuron

One of the most interesting domains of feedforward networks is the processing of sensor signals. There do exist some networks which extract most of the information by implementing the maximum entropy principle for Gaussian sources. This is done by transforming input patterns to the base of eigenvectors of the input autocorrelation matrix with the biggest eigenvalues. The basic building block of these networks is the linear neuron, learning with the Oja learning rule. Nevertheless, some researchers in pattern recognition theory claim that for pattern recognition and classification clustering transformations are needed which reduce the intra-class entropy. This leads to stable, reliable features and is implemented for Gaussian sources by a linear transformation using the eigenvectors with the smallest eigenvalues. In another paper (Brause 1992) it is shown that the basic building block for such a transformation can be implemented by a linear neuron using an Anti-Hebb rule and restricted weights. This paper shows the analog VLSI design for such a building block, using standard modules of multiplication and addition. The most tedious problem in this VLSI-application is the design of an analog vector normalization circuitry. It can be shown that the standard approaches of weight summation will not give the convergence to the eigenvectors for a proper feature transformation. To avoid this problem, our design differs significantly from the standard approaches by computing the real Euclidean norm. Keywords: minimum entropy, principal component analysis, VLSI, neural networks, surface approximation, cluster transformation, weight normalization circuit

FEEDFORWARD ARTIFICIAL NEURAL NETWORK DESIGN UTILISING SUBTHRESHOLD MODE CMOS DEVICES

This thesis reviews various previously reported techniques for simulating artificial neural networks and investigates the design of fully-connected feedforward networks based on MOS transistors operating in the subthreshold mode of conduction as they are suitable for performing compact, low power, implantable pattern recognition systems. The principal objective is to demonstrate that the transfer characteristic of the devices can be fully exploited to design basic processing modules which overcome the linearity range, weight resolution, processing speed, noise and mismatch of components problems associated with weak inversion conduction, and so be used to implement networks which can be trained to perform practical tasks. A new four-quadrant analogue multiplier, one of the most important cells in the design of artificial neural networks, is developed. Analytical as well as simulation results suggest that the new scheme can efficiently be used to emulate both the synaptic and thresholding functions. To complement this thresholding-synapse, a novel current-to-voltage converter is also introduced. The characteristics of the well known sample-and-hold circuit as a weight memory scheme are analytically derived and simulation results suggest that a dummy compensated technique is required to obtain the required minimum of 8 bits weight resolution. Performance of the combined load and thresholding-synapse arrangement as well as an on-chip update/refresh mechanism are analytically evaluated and simulation studies on the Exclusive OR network as a benchmark problem are provided and indicate a useful level of functionality. Experimental results on the Exclusive OR network and a 'QRS' complex detector based on a 10:6:3 multilayer perceptron are also presented and demonstrate the potential of the proposed design techniques in emulating feedforward neural networks

Configurable Low Power Analog Multilayer Perceptron

A configurable, low power analog implementation of a multilayer perceptron (MLP) is presented in this work. It features a highly programmable system that allows the user to create a MLP neural network design of their choosing. In addition to the configurability, this neural network provides the ability of low power operation via analog circuitry in its neurons. The main MLP system is made up of 12 neurons that can be configurable to any number of layers and neurons per layer until all available resources are utilized. The MLP network is fabricated in a standard 0.13 μm CMOS process occupying approximately 1 mm2 of on-chip area. The MLP system is analyzed at several different configurations with all achieving a greater than 1 Tera-operations per second per Watt figure of merit. This work offers a high speed, low power, and scalable alternative to digital configurable neural networks

Low power CMOS analog multipliers.

CMOS analog multiplier is a very important building block and programming element in analog signal processing. Although high-performance multipliers using bipolar transistors have been available for 40 years, CMOS multiplier implementation is still a challenging subject especially for low-power and low-noise circuit design. Since the supply voltage is normally fixed for analog multiplier structures, we use the total current to represent the power dissipation. Our basic idea for low power design of analog multipliers is to fit most of the transistors into the linear region, while at the same time keeping the drain-to-source voltage as low as possible to decease the drain current. And also, we use PMOS transistors for the devices working in the saturation region to further decrease the drain current and improve the linearity performance. Two low power CMOS analog multiplier designs have been proposed in this thesis. We gave detailed performance analysis and some design considerations for these structures. Cadence Hspice simulation verified our analysis. To ensure a fair comparison, we also simulated the performance of a previous multiplier structure, which was considered to be one of the best multiplier structures with low power and low noise performance. Extensive experiments and comparison for these structures show that the proposed CMOS analog multipliers have much less power dissipation than that of previous structures, while at the same time, satisfying other performance requirements. The proposed analog multipliers would be good choices in the applications where low power dissipation is an important consideration.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .L5. Source: Masters Abstracts International, Volume: 43-01, page: 0280. Adviser: Chunhong Chen. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004