High performance interconnection architecture for large cellular neural networks

We describe the analog CMOS VLSI implementation of a cellular neural network (CNN) architecture which spatially filters a 2D image by two orientation selective image filters. The image is represented by a set of input currents supplied by an on-chip array of photosensors. The filters are similar to even and odd Gabor filters. The CNN architecture is implemented using two resistive networks coupled by transconductance amplifiers. The tuned orientation can be steered and the filter response scaled by adjusting the conductances of the resistors and gains of the transconductance amplifiers through externally supplied bias voltages. The circuit operation is explained via a variational principle, which defines the filter output as the minimum of a cost function. We report test results from both 25 × 25 and 45 × 45 pixel arrays.

We describe a novel cellular connectionist neural network model for the implementation of clustering-based Bayesian image segmentation with Gibbs random-field spatial constraints. The success of this algorithm is largely due to the neighborhood constraints modeled by the Gibbs random field. However, the iterative enforcement of the neighborhood constraints involved in the Bayesian estimation would generally require tremendous computational power. Such computational requirement hinders the real-time application of the Bayesian image segmentation algorithms. The cellular connectionist model proposed aims at implementing the Bayesian image segmentation with real-time processing potentials. With a cellular neural network architecture mapped onto the image spatial domain, the powerful Gibbs spatial constraints are realized through the interactions among neurons connected through their spatial cellular layout. This network model is structurally similar to the conventional cellular network. However, in this new cellular model, the processing elements designed within the connectionist network are functionally more versatile to meet the challenging needs of Bayesian image segmentation based on the Gibbs random field. We prove that this cellular neural network does converge to the desired steady state with a properly designed update scheme. Examples of CT volumetric medical image segmentation are presented to demonstrate the potential of this cellular neural network for a specific image segmentation application.

In this paper, an original architecture of cellular neural network (CNN) vision chip is addressed. In the introduction, an analyse of the limitations of the usual approaches leads us to propose an original architecture. The paper is dedicated to the description of the three main blocks of our vision chip. Then, the major building blocks are detailed. Finally, design considerations and the practical implementation of a typical CNN algorithm are discussed.

An efficient digital architecture for the discrete-time cellular neural networks (DTCNNs) is proposed that employs the distributed arithmetic (DA). It consumes little silicon area because of the bit serial computation of DA, and offers higher speed operation than the analogue implementations of DTCNN. The proposed architecture has been implemented in a 0.8 µm CMOS technology.

Reactors with heavy water coolants and moderators have been used extensively in today's power industry. Monitoring of the moderator condition plays an important role in ensuring normal operation of a power plant. A cellular neural network, the architecture of which has been adapted for hardware implementation, is proposed for use in a system for prediction of the heavy water moderator temperature. A reactor model composed in accordance with the CANDU Darlington heavy water reactor design was used to form the training sample collection and to control correct operation of the neural network structure. The sample components for the adjustment and configuration of the network topology include key parameters that characterize the energy generation process in the core. The paper considers the feasibility of the temperature prediction only for the calandria's central cross-section. To solve this problem, the cellular neural network architecture has been designed, and major parts of the digital computational element and methods for their implementation based on an FPLD have also been developed. The method is described for organizing an optical coupling between individual neural modules within the network, which enables not only the restructuring of the topology in the training process, but also the assignment of priorities for the propagation of the information signals of neurons depending on the activity in a situation analysis at the neural network structure inlet. Asynchronous activation of cells was used based on an oscillating fractal network, the basis for which was a modified ring oscillator. The efficiency of training the proposed architecture using stochastic diffusion search algorithms is evaluated. A comparative analysis of the model behavior and the results of the neural network operation have shown that the use of the neural network approach is effective in safety systems of power plants.

We present the packet wavelet coder implemented with cellular neural network architecture, and show its superiority over the pyramidal wavelet representation. This paper also demonstrates how the cellular neural universal machine (CNNUM) architecture can be extended to image compression. The packet wavelet coder performs the operation of image compression, aided by CNN architecture. It uses the highly parallel nature of the CNN structure and its speed outperforms traditional digital computers. In packet wavelet coder, an image signal can be analyzed by passing it through an analysis filter banks followed by a decimation process, according to the rules of packet wavelets. The Simulation results indicate that the quality of the reconstructed image is superior by using packet wavelet coding scheme. Finally, a quantization operation is performed in order to translate the coefficient values to discrete environment. Our results are compared with that of pyramidal wavelet representation

An analog Cellular Neural Network (CNN) architecture employing quantum dots to realize various real time image processing applications such as edge and line detections and motion estimation is proposed. In order to obtain programmability to switch between applications, memristive connections between neighboring cells, and for signal amplification and locking resonant tunneling diodes (RTDs) are utilized. Simulations are carried out on a 2D array of the proposed cell structure to demonstrate edge detection and line detection tasks. This work also provides analytical models and simulation results to prove above mentioned real time image processing functionalities.

In this paper the implementation of a nonlinear wave metric on the 64/spl times/64 I/O CNN-UM chip and its experimental results are presented. The nonlinear wave metric was designed and introduced as a generalized theorem for object analysis and classification. This proposed metric includes the well-known distance measures such as Hamming, Hausdorff metrics as special cases. The defined computational method is well-suited for cellular neural network (CNN) architecture and the experimental results shows good correlation with theoretic considerations.

We present the pyramidal wavelet coder implemented with a Cellular Neural Network architecture, as an example of a Cellular Neural Network application, considering that some times it is extremely desired the massive and real-time processing and this kind of architecture fits very well for such purposes. The pyramidal wavelet coder works performing the image wavelet transform plus threshold and quantization operations. The wavelet transform consists essentially in a bank of filters, where an image is passed through them repeatedly, and after each filtering a sampling operation is performed. Once image has been filtered and sampled according the rules of the pyramidal image coder, the threshold operation is carried out, where we pretend to keep only the most significant wavelet coefficients. Finally, a quantization operation is performed in order to translate the coefficient values to a discrete environment.

We present frequency and spatial adaptive packet wavelet coder implemented with cellular neural network architecture. This paper also demonstrates how the cellular neural universal machine (CNNUM) architecture can be extended to image compression. The packet wavelet coder performs the operation of image compression, aided by CNN architecture. It uses the highly parallel nature of the CNN structure and its speed outperforms traditional digital computers. In packet wavelet coder, an image signal can be analyzed by passing it through an analysis filter banks followed by a decimation process, according to the rules of packet wavelets. Traditional packet decomposition adapts to a global frequency distribution, this technique finds the best joint of spatial segmentation and local frequency basis. The simulation results indicate that the quality of the reconstructed image is superior by using frequency and spatial adaptive packet wavelet coding scheme.

The development of new approaches and new architectures for locomotion control in legged robots is of high interest in the area of robotic and intelligent motion systems, especially when the solution is easy both to conceive and to implement.This first lecture emphasizes analog neural processing structures to realize artificial locomotion in mechatronic devices. The main inspiration comes from the biological paradigm of the Central Pattern Generator (CPG), used to model the neural populations responsible for locomotion planning and control in animals. The approach presented here starts by considering locomotion by legs as a complex spatio-temporal non linear dynamical system, modelled referring to particular types of reaction-diffusion non linear partial differential equations. In the following lecture these Spatio-temporal phenomena are obtained implementing the whole mathematical model on a new Reaction-Diffusion Cellular Neural Network (RD-CNN) architecture. Wave-like solutions as well as patterns are obtained, able to induce and control locomotion in some prototypes of biologically inspired walking machines. The design of the CNN structure is subsequently realized by analog circuits; this gives the possibility to generate locomotion in real time and also to control the transition among several types of locomotion. The methodology presented is applied referring to the experimental prototype of an hexapod robot. In the last lecture the same approach will be shown to be able to realize locomotion generation and control in a number of different robotic structures, such as ring worm-like robots or lamprey-like robots.KeywordsCentral Pattern GeneratorAnalog CircuitTuring PatternLocomotion ControlHexapod RobotThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The design of neural network models involves numerous complexities, including the determination of input vectors, choosing the number of hidden layers and their computational units, and specifying activation functions for the latter. The combinatoric possibilities are daunting, yet experience has yielded informal guidelines that can be useful. Alternatively, current research on genetic algorithms (GA) suggests that they might be of practical use as a formal method of determining ‘good’ architectures for neural networks. In this paper, we use a genetic algorithm to find effective architectures for backpropagation neural networks (BP). We compare the performance of heuristically designed BP networks with that of GA-designed BP networks. Our test domains are sets of problems having compensatory, conjunctive, and mixed-decision structures. The results of our experiment suggest that heuristic methods produce architectures that are simpler and yet perform comparatively well. © 1998 John Wiley & Sons, Ltd.

Nowadays deep neural networks are widely used to accurately classify input data. An interesting application area is the Internet of Things (IoT), where a massive amount of sensor data has to be classified. The processing power of the cloud is attractive, however the variable latency imposes a major drawback in situations where near real-time classification is required. In order to exploit the apparent trade-off between utilizing the stable but limited embedded computing power of IoT devices and the seemingly unlimited computing power of Cloud computing at the cost of higher and variable latency, we propose a Big-Little architecture for deep neural networks. A small neural network trained to a subset of prioritized output classes is running on the embedded device, while a more specific classification is calculated when required by a large neural network in the cloud. We show the applicability of this concept in the IoT domain by evaluating our approach for state of the art neural network classification problems on popular embedded devices such as the Raspberry Pi and Intel Edison.

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers’ view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.

Nowadays with the deployment of a large and dense heterogeneous networks more sophisticated algorithms for resource scheduling are needed. Implementing hard coded scheduling algorithms without taking into account the very specific dynamic of the traffic generated by the mobile users can lead to a network performance quite far from the optimal. By using novel machine learning (ML) algorithms we can store not only the raw traffic data and its variations but also build the so-called heat maps, reflecting the changes of the traffic over time, space and per user. Using neural network (NN) architectures, trained by the raw data statistics, we can store the network traffic model at minimum data storage without the need of keeping and looking up at the raw data. Using such NN architecture the network state in next time intervals could be predicted and this prediction used for decision making about how the network resources to be scheduled among the active mobile users. To implement adaptive resource scheduling named “AdaptSch” a neural network architecture with two main blocks is proposed. The simulation results show that by incorporating a neural classifier for adapting the resource scheduler we can utilize the advantages and the effectiveness of multiple scheduler algorithms and improve overall throughput and packet delay.