Mixed-Signal Neural Network Implementation with Programmable Neuron

This thesis introduces implementation of mixed-signal building blocks of an artificial neural network; namely the neuron and the synaptic multiplier. This thesis, also, investigates the nonlinear dynamic behavior of a single artificial neuron and presents a Distributed Arithmetic (DA)-based Finite Impulse Response (FIR) filter. All the introduced structures are designed and custom laid out

A Mixed-Signal Feed-Forward Neural Network Architecture Using A High-Resolution Multiplying D/A Conversion Method

Artificial Neural Networks (ANNs) are parallel processors capable of learning from a set of sample data using a specific learning rule. Such systems are commonly used in applications where human brain may surpass conventional computers such as image processing, speech/character recognition, intelligent control and robotics to name a few. In this thesis, a mixed-signal neural network architecture is proposed employs a high resolution Multiplying Digital to Analog Converter (MDAC) designed using Delta Sigma Modulation (DSM). To reduce chip are, multiplexing is used in addition to analog implementation of arithmetic operations. This work employs a new method for filtering the high bit-rate signals using neurons nonlinear transfer function already existing in the network. Therefore, a configuration of a few MOS transistors are replacing the large resistors required to implement the low-pass filter in the network. This configuration noticeably decreases the chip area and also makes multiplexing feasible for hardware implementation

An investigation into adaptive power reduction techniques for neural hardware

In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing field [1] and the present thrust of the semiconductor industry towards lowpower SOCs for mobile devices [2], the power consumption of ANN hardware has become a very important implementation issue. Adaptability is a powerful and useful feature of neural networks. All current approaches for low-power ANN hardware techniques are ‘non-adaptive’ with respect to the power consumption of the network (i.e. power-reduction is not an objective of the adaptation/learning process). In the research work presented in this thesis, investigations on possible adaptive power reduction techniques have been carried out, which attempt to exploit the adaptability of neural networks in order to reduce the power consumption. Three separate approaches for such adaptive power reduction are proposed: adaptation of size, adaptation of network weights and adaptation of calculation precision. Initial case studies exhibit promising results with significantpower reduction

FPGA implementation of an image recognition system based on tiny neural networks and on-line reconfiguration

Neural networks are widely used in pattern recognition, security applications and robot control. We propose a hardware architecture system; using Tiny Neural Networks (TNN) specialized in image recognition. The generic TNN architecture allows expandability by means of mapping several Basic units (layers) and dynamic reconfiguration; depending on the application specific demands. One of the most important features of Tiny Neural Networks (TNN) is their learning ability. Weight modification and architecture reconfiguration can be carried out at run time. Our system performs shape identification by the interpretation of their singularities. This is achieved by interconnecting several specialized TNN. The results of several tests, in different conditions are reported in the paper. The system detects accurately a test shape in almost all the experiments performed. The paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and was configured as a perceptron network with backpropagation learning and applied to the recognition of shapes. Simulation results show that this architecture has significant performance benefits