A programmable VLSI filter architecture for application in real-time vision processing systems

An architecture is proposed for the realization of real-time edge-extraction filtering operation in an Address-Event-Representation (AER) vision system. Furthermore, the approach is valid for any 2D filtering operation as long as the convolutional kernel F(p,q) is decomposable into an x-axis and a y-axis component, i.e. F(p,q)=H(p)V(q), for some rotated coordinate system [p,q]. If it is possible to find a coordinate system [p,q], rotated with respect to the absolute coordinate system a certain angle, for which the above decomposition is possible, then the proposed architecture is able to perform the filtering operation for any angle we would like the kernel to be rotated. This is achieved by taking advantage of the AER and manipulating the addresses in real time. The proposed architecture, however, requires one approximation: the product operation between the horizontal component H(p) and vertical component V(q) should be able to be approximated by a signed minimum operation without significant performance degradation. It is shown that for edge-extraction applications this filter does not produce performance degradation. The proposed architecture is intended to be used in a complete vision system known as the Boundary-Contour-System and Feature-Contour-System Vision Model, proposed by Grossberg and collaborators. The present paper proposes the architecture, provides a circuit implementation using MOS transistors operated in weak inversion, and shows behavioral simulation results at the system level operation and electrical simulation and experimental results at the circuit level operation of some critical subcircuits

Algorithms for VLSI stereo vision circuits applied to autonomous robots

Since the inception of Robotics, visual information has been incorporated in order to allow the robots to perform tasks that require an interaction with their environment, particularly when it is a changing environment. Depth perception is a most useful information for a mobile robot to navigate in its environment and interact with its surroundings. Among the different methods capable of measuring the distance to the objects in the scene, stereo vision is the most advantageous for a small, mobile robot with limited energy and computational power. Stereoscopy implies a low power consumption because it uses passive sensors and it does not require the robot to move. Furthermore, it is more robust, because it does not require a complex optic system with moving elements. On the other hand, stereo vision is computationally intensive. Objects in the scene have to be detected and matched across images. Biological sensory systems are based on simple computational elements that process information in parallel and communicate among them. Analog VLSI chips are an ideal substrate to mimic the massive parallelism and collective computation present in biological nervous systems. For mobile robotics they have the added advantage of low power consumption and high computational power, thus freeing the CPU for other tasks. This dissertation discusses two stereoscopic methods that are based on simple, parallel cal- culations requiring communication only among neighboring processing units (local communication). Algorithms with these properties are easy to implement in analog VLSI and they are also very convenient for digital systems. The first algorithm is phase-based. Disparity, i.e., the spatial shift between left and right images, is recovered as a phase shift in the spatial-frequency domain. Gábor functions are used to recover the frequency spectrum of the image because of their optimum joint spatial and spatial-frequency properties. The Gábor-based algorithm is discussed and tested on a Khepera miniature mobile robot. Two further approximations are introduced to ease the analog VLSI and digital implementations. The second stereoscopic algorithm is difference-based. Disparity is recovered by a simple calculation using the image differences and their spatial derivatives. The algorithm is simulated on a digital system and an analog VLSI implementation is proposed and discussed. The thesis concludes with the description of some tools used in this research project. A stereo vision system has been developed for the Webots mobile robotics simulator, to simplify the testing of different stereo algorithms. Similarly, two stereo vision turrets have been built for the Khepera robot

Design and implementation of a digital neural processor for detection applications

The main focus of this research is to develop a digital neural network (processor) and hardware (VLSI) implementation of the same for detection applications, for example in the distance protection of power transmission lines. Using a hardware neural processor will improve the protection system performance over software implementations in terms of speed of operation, response time for faults etc. The main aspects of this research are software design, performance analysis, hardware design and hardware implementation of the digital neural processor. The software design is carried out by developing an object oriented neural network simulator with backpropagation training using C++ language. A preliminary analysis shows that the inputs to the neural network need to be preprocessed. Two filters have been developed for this purpose, based on the analysis of the training data available. The performance analysis involves studying quantization effects (determination of precision requirements) in the network. -- The hardware design involves design of the neural network and the preprocessors. The neural processor consists of three types of processing elements (neurons): input, hidden and output neurons. The input neurons form the input layer of the processor which receive input from the preprocessors. The input layer can be configured to directly receive external input by changing the mode of operation. The output layer gives the signal to the relay for tripping the line under fault. Each neuron consists of datapath and local control unit. Datapath consists of the components for forward and backward passes of the processor and the register file. The local control unit controls the flow of data within a neuron and co-ordinates with the global control unit which controls the flow of data between layers. The neurons and the layers are pipelined for improving the throughput of the processor. The neural processor and the filters are implemented in VLSI using hardware description language (VHDL) and Synopsys / Cadence CAD tools. All the components are individually verified and tested for their functionality and implemented using 0.5 μ CMOS technology

Digital control networks for virtual creatures

Robot control systems evolved with genetic algorithms traditionally take the form of floating-point neural network models. This thesis proposes that digital control systems, such as quantised neural networks and logical networks, may also be used for the task of robot control. The inspiration for this is the observation that the dynamics of discrete networks may contain cyclic attractors which generate rhythmic behaviour, and that rhythmic behaviour underlies the central pattern generators which drive lowlevel motor activity in the biological world. To investigate this a series of experiments were carried out in a simulated physically realistic 3D world. The performance of evolved controllers was evaluated on two well known control tasks—pole balancing, and locomotion of evolved morphologies. The performance of evolved digital controllers was compared to evolved floating-point neural networks. The results show that the digital implementations are competitive with floating-point designs on both of the benchmark problems. In addition, the first reported evolution from scratch of a biped walker is presented, demonstrating that when all parameters are left open to evolutionary optimisation complex behaviour can result from simple components