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Analog computational circuits for neural network implementations

Analog computational circuits for neural network implementat...

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IEEE International Conference on Electronics, Circuits and systems (ICECS)

作者： M. Al-Nsour H.S. Abdel-Aty-Zohdy Microelectronics System Design Laboratory Department of Electrical and Systems Engineering Oakland University Rochester MI USA

High performance NMOS analog circuits which perform voltage addition, average weighing, and signal scaling are presented. Utilization of these circuits in artificial neural network applications is briefly discussed. The circuits are characterized by a simple structure which offers compactness, low voltage supply, and wide input range. In addition, inputs and outputs are voltages which eliminate the need for current voltage conversion. Hence, reduced power consumption and higher performance is achieved. A two-input analog adder has been accomplished through MOSIS 2.0 /spl mu/m n-well CMOS technology at Orbit Semiconductors, and occupies an area of 135/spl times/145 /spl mu/m/sup 2/. Inputs are within /spl plusmn/3 V for 3.3 V supply. Power consumption is less than 0.5 mW and THD is less than -36 dB. Measurements and performance evaluation are presented.

关键词： Analog computers Computer networks Neural networks Artificial neural networks Energy consumption CMOS technology MOS devices Analog circuits Low voltage Adders

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Reinforcement learning neural network circuits for electronic nose

Reinforcement learning neural network circuits for electroni...

引用

IEEE International Symposium on Circuits and systems (ISCAS)

作者： H.S. Abdel-Aty-Zohdy M. Al-Nsour Microelectronics System Design Laboratory Department of Electrical and Systems Engineering Oakland University Rochester MI USA

This paper approaches electronic nose design from two promising angles: reinforcement neural network (RNN) learning algorithms, and naturally compatible analog circuits. This approach is inspired by biological sensing and discrimination of a multitude of odors in a background environment. A VLSI system approach is presented for classification of chemical compounds, with knowledge of key features only. Based on utilizing microsensor arrays, reinforcement neural networks are used to affect nonparametric pattern recognition, classification, and distinction among multicomponent chemicals. A specialized RNN approach is chosen. Realization and implementation of analog RNN circuits is presented using 1.2 /spl mu/m CMOS n-well technology, at AMI, through the MOSIS facilities. Preliminary results are satisfactory and lend evidence to the effectiveness of the analog designed neural network building blocks for temporal and spatial NN pattern recognition.

关键词： Learning Neural networks Electronic noses Recurrent neural networks Pattern recognition CMOS technology Algorithm design and analysis Analog circuits Very large scale integration Chemical compounds

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ANN digitally programmable analog synapse

ANN digitally programmable analog synapse

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Midwest Symposium on Circuits and systems (MWSCAS)

作者： M. Al-Nsour H. Abdel-Aty-Zhody Microelectronics System Design Laboratory Department of Electrical and Systems Engineering Oakland University Rochester MI USA Oakland University Rochester MI US

An Artificial Neural Network (ANN) synapse with local digital weight storage capability is presented. It utilizes a novel, compact, voltage mode multiplying digital-to-analog converter to perform multiplication between an analog input and a digital weight. A 6-bit signed weight synapse chip has been implemented in VLSI through MOSIS 1.2 micrometer n-well CMOS technology. It occupies a total area of 1.35 mm/sup 2/. With +3 Volts supply, the circuit accepts analog inputs within /spl plusmn/1 V. Output is also within /spl plusmn/1 V. Maximum power consumption is 20 mW and total error is less than 3%. Circuit analysis and performance evaluation is presented.

关键词： Voltage Circuits Artificial neural networks Digital-analog conversion CMOS technology Energy consumption Silicon Capacitance microelectronics Laboratories

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Implementation aspects of turbo-decoders for future radio applications

Implementation aspects of turbo-decoders for future radio ap...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： F. Berens A. Worm H. Michel N. Wehn Advanced System Technology Geneva Applications Laboratory ST Microelectronics N.V. Geneva Switzerland Department of Electrical Engineering Institute of Microelectronic Systems University of Kaiserslautern Kaiserslautern Germany

Turbo codes will most likely be employed in future radio systems as a channel coding scheme for high-rate data services. However, turbo decoding is a comparatively complex task. To obtain efficient decoder implementations, the system design space has to be explored on multiple levels. In this paper, we span the system design space for turbo codes and describe a method of exploration, while focusing on the implementation-dependent part. The design decisions taken during exploration are rated regarding complexity, throughput and power consumption. The second part of our paper evaluates sample software and hardware implementations of a 2 Mbit/s turbo decoder.

关键词： Iterative decoding Turbo codes Hardware Viterbi algorithm Space technology Throughput Energy consumption Convolutional codes Iterative algorithms Computer architecture

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Verification system interface for VLSI combinational circuits

Verification system interface for VLSI combinational circuit...

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1998 Midwest Symposium on Circuits and systems, MWSCAS 1998

作者： El-Licy, Fatma A. Abdel-Aty-zohdy, Hoda S. Department of Electrical and Systems Engineering Microelectronics System Design Laboratory Oakland University RochesterMI48309-4401 United States

ISBN: (纸本)0818689145

A proof of correctness of a given VLSIC system design is established by proving the consistency and implication of the implemented design with respect to the specified design. The set of conditions that establish such consistency is termed verification conditions (VCs). This paper presents an automatic interface system for the verification of VLSI combinational circuits in which VCs are synthesized from circuit definition. This interface will allow a top down specification of the design (written in a given HDL language) to be parsed into binary tree structure representation. Also, a logical construct is built up from the physical layout specifications of the implemented design (extracted as SPICE file). The top down parse tree along with bottom up logic constructs form the basic building block structure of the front end of the interface system. VCs are then synthesized from the outcomes of the front end through a preprocessor. The back end of the interface system constitutes the preprocessor and a set of designators and modules for manipulating mathematical and logical operations. It provides an environment in which a theorem prover can be used to create a formal proof schema for the VCs which satisfy partial correctness of the circuit under consideration. © 1999 IEEE.

关键词： Specifications

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Implementation of programmable digital sigmoid function circuit for neuro-computing

Implementation of programmable digital sigmoid function circ...

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1998 Midwest Symposium on Circuits and systems, MWSCAS 1998

作者： Al-Nsour, Mahmoud Abdel-Aty-zohdy, Hoda S. Department of Electrical and Systems Engineering Microelectronics Systems Design Laboratory Oakland University RochesterMI48309-4401 United States

ISBN: (纸本)0818689145

A programmable digital sigmoid function generator circuit has been designed and implemented. The circuit is based on second order approximation of the nonlinear sigmoid function and utilizing its characteristics. It offers an easy way of changing the steepness of the function in the transition state. In addition, the squaring function operation was implemented using combinational logic. This offers a compact realization of the system as a parallel bit architecture. The maximum output error is.047 and the average error is within 0.01. Simulations show that the circuit operates satisfactorily up to 50 MHz. The digital VLSI circuit has been fabricated using MOSIS/AMI N-well 1.2 μm CMOS double metal process. It occupies an area of 900×450 μm2. © 1999 IEEE.

关键词： Timing circuits

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Artificial neural network electronic nose for volatile organic compounds

Artificial neural network electronic nose for volatile organ...

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Great Lakes Symposium on VLSI

作者： H.S. Abdel-Aty-Zohdy Microelectronics System Design Lab Department of Electrical and Systems Engineering Oakland University Rochester MI USA

Advanced microsystems that include, sensors, interface-circuits, and pattern-recognition integrated monolithically or in a hybrid module are needed for civilian, military, and space applications. These include: automotive, medical applications, environmental engineering, and manufacturing automation. ASICs with Artificial Neural Networks (ANN) are considered in this paper, with the objective of recognizing air-borne volatile organic compounds, especially alcohols, ethers, esters, halocarbons, NH/sub 3/, NO/sub 2/, and other warfare agent simulants. The ASIC inputs are connected to the outputs from array-distributed sensors which measure three-features for identifying each of four chemicals. A Specialized Reinforcement Neural Network (RNN) learning approach is chosen for the chemicals classification problem. Hardware implementation of the RNN is presented for 2 /spl mu/m CMOS process, MOSIS chip. design implementation and evaluation are also presented.

关键词： Artificial neural networks Electronic noses Sensor arrays Automotive engineering Chemical sensors Recurrent neural networks Medical services Biomedical equipment Biomedical engineering Manufacturing automation

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Wavelet-multifractal processing of signals

Wavelet-multifractal processing of signals

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IEEE Asia-Pacific Conference on Circuits and systems

作者： K. Soemintapura A.Z.R. Langi Laboratory for Signals and Systems Department of Electrical Engineering Institute of Technology Bandung Laboratory of Microelectronics Design IURC Microelectronics Institute of Technology Bandung Bandung West Java Indonesia

This research develops a new approach in processing multimedia signals (i.e., speech, audio, and images) using wavelets and multifractals. Processing multimedia signals is critical in many modern telecommunication and information systems. Nonstationary behaviours have been the most important characteristics of multimedia signals because they carry signal information. Unfortunately nonstationary parts are difficult to process effectively using traditional tools such as Fourier transforms. In contrast, wavelets and multifractality not only reveal that nonstationary parts are sufficient to represent natural images, but also can be used to reconstruct such signals. The general objectives of this research are to develop the wavelet and multifractal concepts into a new signal-processing framework, to obtain more effective and efficient signal representation. If the objectives can be achieved, the results of the proposed research can be a breakthrough in nonstationary signal processing, as fundamental as the well known Fourier approach in stationary signal processing.

关键词： Signal processing Feature extraction Fractals Signal analysis Laboratories microelectronics Signal synthesis Character recognition Data mining Signal design

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Reinforcement neural learning with application to gas sensors

Reinforcement neural learning with application to gas sensor...

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Midwest Symposium on Circuits and systems (MWSCAS)

作者： H.S. Abdel-Aty-Zohdy Microelectronics System Design Laboratory Department of Electrical and Systems Engineering Oakland University Rochester MI USA

Neural net reinforcement learning algorithm and its application to gas (chemicals) classification and quantification are considered in this paper. The approach does not require memory and has simplified reward/punishment computations. The advantages and disadvantages of the reinforcement algorithm are contrasted against other competing neural algorithms. The reinforcement learning has been found practical, when each gas (chemical) is distinguished by several features and characteristics with possible overlap. The paper also gives an account of integrated circuit digital chip implementation for typical four gases with temperature, pressure, and flow quantity features. The number of required iterations has been found to depend on reward and penalty parameters, as well as the threshold governing the learning. It is believed that our implementation approach has potential uses in auto industry safety and emission control, as well in automated semiconductor manufacturing process.

关键词： Gas detectors Learning Chemicals Neural networks Digital integrated circuits Gases Temperature Industrial control Manufacturing industries Safety

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A wide input range analog multiplier for neuro-computing

A wide input range analog multiplier for neuro-computing

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Midwest Symposium on Circuits and systems (MWSCAS)

作者： M. Al-Nsour H.S. Abdel-Aty-Zohdy Microelectronics Systems Design Laboratory Department of Electrical and Systems Engineering Oakland University Rochester MN USA Microelectronics Systems Design Laboratory Department of Electrical and Systems Engineering Oakland University Rochester MI USA

A wide input range 4-quadrant analog multiplier design and its VLSI implementation are presented. The design is based on the squaring law operation of saturated NMOS transistors. The circuit is realized by using 12 NMOS and 2 PMOS transistors. The multiplier accepts two signed inputs in the range of /spl plusmn/4 V. The simulated corner frequency is found to be at 85 MHz. N-well 2 /spl mu/m CMOS double metal process is utilized. It occupies 99/spl times/160 /spl mu/m/sup 2/. Statistical linear regression showed that the multiplier has less than 1% overall error. By extending the input range and minimizing the silicon area, this design has high potential in evolving neuro-computing applications.

关键词： MOSFETs Silicon Neural networks Neurons Intrusion detection Threshold voltage Circuit analysis design optimization microelectronics Laboratories

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