Neural Network-Based Analog-to-Digital Converters

In this chapter, we present an overview of the recent advances in analog-to-digital converter (ADC) neural networks. Biological neural networks consist of natural binarization reflected by the neurosynaptic processes. This natural analog-to-binary conversion ability of neurons can be modeled to emulate analog-to-digital conversion using a set of nonlinear circuit elements and existing artificial neural network models. Since one neuron during processing consumes on average only about half nanowatts of power, neurons can perform highly energy-efficient operations, including pattern recognition. Analog-to-digital conversion itself is an example of simple pattern recognition where input analog signal can be presented in one of the 2N different patterns for N bits. The classical configuration of neural network-based ADC is Hopfield neural network ADC. Improved designs, such as modified Hopfield network ADC, T-model neural ADC, and multilevel neurons-based neural ADC, will be discussed. In addition, the latest architecture designs of neural ADC such as hybrid complementary metal-oxide semiconductor (CMOS)-memristor Hopfield ADC are covered at the end of this chapter

An Investigation into Neuromorphic ICs using Memristor-CMOS Hybrid Circuits

The memristance of a memristor depends on the amount of charge flowing through it and when current stops flowing through it, it remembers the state. Thus, memristors are extremely suited for implementation of memory units. Memristors find great application in neuromorphic circuits as it is possible to couple memory and processing, compared to traditional Von-Neumann digital architectures where memory and processing are separate. Neural networks have a layered structure where information passes from one layer to another and each of these layers have the possibility of a high degree of parallelism. CMOS-Memristor based neural network accelerators provide a method of speeding up neural networks by making use of this parallelism and analog computation. In this project we have conducted an initial investigation into the current state of the art implementation of memristor based programming circuits. Various memristor programming circuits and basic neuromorphic circuits have been simulated. The next phase of our project revolved around designing basic building blocks which can be used to design neural networks. A memristor bridge based synaptic weighting block, a operational transconductor based summing block were initially designed. We then designed activation function blocks which are used to introduce controlled non-linearity. Blocks for a basic rectified linear unit and a novel implementation for tan-hyperbolic function have been proposed. An artificial neural network has been designed using these blocks to validate and test their performance. We have also used these fundamental blocks to design basic layers of Convolutional Neural Networks. Convolutional Neural Networks are heavily used in image processing applications. The core convolutional block has been designed and it has been used as an image processing kernel to test its performance.Comment: Bachelor's thesi

Projeto de um ADC baseado numa rede neuronal de Hopfield

Todos os dias se assiste à superação de novas barreiras em campos como os sistemas computacionais e as suas capacidades de processamento. Tais avanços levam a que a procura por conversores analógico-digitais mais eficientes, com maior resolução, de elevado desempenho e versáteis seja, atualmente, maior do que nunca. Os conversores analógico-digitais baseados na arquitetura apresentada por Hopfield, são uma tecnologia que surge como uma forte candidata na resposta a essa procura. Os SAR ADCs têm-se revelado os conversores eleitos em aplicações que exigem reduzida área física e reduzido consumo energético. Como tal, desta dissertação resulta um SAR ADC de arquitetura “desenrolada” com múltiplos DACs, com 4 bits de resolução, robusto a erros causados pela tensão de desvio dos comparadores e erros de emparelhamento dos condensadores

On neural networks for analog-to-digital conversion

In this paper we compare analog to,digital conversion (ADC) delay in Hopfield ADC and asymmetrical (lower triangular) neural network-based ADC due to Avitabile et al. It is shown that, although Hopfield ADC; has extensive feedback, its behavior in asynchronous mode is similar to that of lower triangular ADC. It is also shown that any constant delay n-bit feedforward ADC must have an exponential number of neurons