7 research outputs found
Proposta de implementação em hardware dedicado de redes neurais competitivas com técnicas de circuitos integrados analógicos
Neste trabalho apresenta-se uma proposta de uma técnica para implementação em hardware, das estruturas básicas de uma Rede Neural Competitiva, baseada em técnicas analógicas.
Através desta proposta, será abordada uma das classes mais interessantes de Redes Neurais Artificiais (RNA) que são as Redes Neurais Competitivas (RNC), que possuem forte inspiração biológica. As equações fundamentais que descrevem o comportamento da RNC foram derivadas de estudos interdisciplinares, a maioria envolvendo observações neurofisiológicas. O estudo do neurônio biológico, por exemplo, nos leva à clássica equação da membrana.
A técnica mostrada para a implementação das Redes Neurais Competitivas se baseia no uso das técnicas analógicas. Estas conduzem a um projeto mais compacto além de permitirem um processamento em tempo real, visto que o circuito computacional analógico altera simultaneamente e continuamente todos os estados dos neurônios que se encontram interligados em paralelo.
Para esta proposta de implementação, é mostrado que as equações fundamentais que governam as Redes Neurais Competitivas possuem uma relação com componentes eletrônicos básicos, podendo então, serem implementados através destes simples componentes com os quais as equações fundamentais se relacionam.
Para tanto, é mostrado por meio de simulações em software, o comportamento das equações fundamentais deste tipo de Redes Neurais, e então, é comparado este comportamento, com os obtidos através de simulações elétricas dos circuitos equivalentes oriundos destas equações fundamentais. Mostra-se também, em ambas as simulações, uma das características mais importantes existentes nos modelos de RNC, conhecida como Memória de Tempo Curto (STM).
Por fim, é apresentada uma aplicação típica na área de clusterização de padrões utilizando pesos sinápticos, a fim de, demonstrar a implementação utilizando as técnicas descritas durante o trabalho. Esta aplicação é demonstrada através de uma simulação elétrica, sendo esta realizada por meio do simulador HSPICE. Tal aplicação demonstra o correto desempenho da proposta deste trabalho.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI
Floating-Gate MOS Synapse Transistors
Our goal is to develop silicon learning systems. One impediment to achieving this goal has been the lack of a simple circuit element combining nonvolatile
analog memory storage with locally computed memory updates. Existing circuits [63, 132] typically are large and complex; the nonvolatile floating-gate devices,
such as EEPROM transistors. typically are optimized for binary-valued storage [17], and do not compute their own memory updates. Although floating-gate
transistors can provide nonvolatile analog storage [1, 15], because writing the memory entails the difficult process of moving electrons through Si0_2, these devices have not seen wide use as memory elements in silicon learning systems
Neural Network Adaptations to Hardware Implementations
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential. However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and nonideal responses, and restraining computational complexity. Furthermore, a broad range of hardware-friendly learning rules is presented, which allow for simpler and more reliable hardware implementations. The relevance of these neural network adaptations to hardware is illustrated by their application in existing hardware implementations
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