Gaussian Synapse Research Articles

Gaussian synapses for probabilistic neural networks

The recent decline in energy, size and complexity scaling of traditional von Neumann architecture has resurrected considerable interest in brain-inspired computing. Artificial neural networks (ANNs) based on emerging devices, such as memristors, achieve brain-like computing but lack energy-efficiency. Furthermore, slow learning, incremental adaptation, and false convergence are unresolved challenges for ANNs. In this article we, therefore, introduce Gaussian synapses based on heterostructures of atomically thin two-dimensional (2D) layered materials, namely molybdenum disulfide and black phosphorus field effect transistors (FETs), as a class of analog and probabilistic computational primitives for hardware implementation of statistical neural networks. We also demonstrate complete tunability of amplitude, mean and standard deviation of the Gaussian synapse via threshold engineering in dual gated molybdenum disulfide and black phosphorus FETs. Finally, we show simulation results for classification of brainwaves using Gaussian synapse based probabilistic neural networks.

Unusual human behavior recognition using evolutionary technique

The vision for surveillance is an important task in many computer vision applications. The monitoring system concerns the tracking and recognition of people, and more generally, the understanding of human behaviors, from image sequences involving humans. Several methods for human tracking and human behavior recognition have been proposed by various researchers. But most of those do not have versatility and flexibility. In this paper, we propose an efficient and robust object tracking algorithm which use the color features, the distance features and count feature based on an evolutionary techniques to measure the observation similarity. And then we will track each person and classify their behavior properties by analyzing their trajectory pattern. We propose multi-layer perceptron based on hybrid genetic algorithm using Gaussian synapse make the recognition algorithm very efficient and robust for classify human behavior by trajectory pattern.

Gaussian synapse ANNs in multi- and hyperspectral image data analysis

A new type of artificial neural network is used to identify different crops and ground elements from hyperspectral remote sensing data sets. These networks incorporate Gaussian synapses and are trained using a specific algorithm called Gaussian synapse back propagation described here. Gaussian synapses present an intrinsic filtering ability that permit concentrating on what is relevant in the spectra and automatically discard what is not. The networks are structurally adapted to the problem complexity as superfluous synapses and/or nodes are implicitly eliminated by the training procedure, thus pruning the network to the required size straight from the training set. The fundamental difference between the present proposal and other ANN topologies using Gaussian functions is that the latter use these functions as activation functions in the nodes, while in our case, they are used as synaptic elements, allowing them to be easily shaped during the training process to produce any type of n-dimensional discriminator. This paper proposes a multi- and hyperspectral image segmenter that results from the parallel and concurrent application of several of these networks providing a probability vector that is processed by a decision module. Depending on the criteria used for the decision module, different perspectives of the same image may be obtained. The resulting structure offers the possibility of resolving mixtures, that is, carrying out a spectral unmixing process in a very straightforward manner.

Real time proximity effects neurocorrector

A method for the proximity effects correction in Electron Beam Lithography for layouts with critical dimensions below 180nm is proposed. A parallel processing system based on an artificial neural networks is suggested as a solution to the problem. The synthesis of the architecture as well as the training algorithms for such neurocorrection system are presented. The algorithm for the learning vector generation is based on a discrete iterative regularisation. Several results of the correction process for different test layouts are presented. Error analysis of the error measure is presented. The difference between the target dose and the doses deposited in each exel after the correction process is smaller than 5%. As an hardware implementation of the real time proximity effects corrector the RBF (radial basis functions) neural system is proposed. Simulations of the Gaussian synapse cell have been done. Results of our simulations assure that our neurocorrector can precompensate for one exel from the layout in less than 60ns.

A programmable CMOS gaussian synapse for analogue VLSI neural networks

Feedforward neural networks with a single hidden layer using a gaussian function have been proven to have the capability of universal approximation in a satisfactory sense. Back-propagation neural networks with gaussian function synapses have a better convergence property over those with linear multiplying synapses. A programmable gaussian synapse for analogue VLSI neural networks with hardware implementation and the programming techniques to program the cell are presented. The standard deviation and the magnitude of the gaussian synapse can be programmed externally. The proposed gaussian synapse was designed with single-ended inputs. To verify the programmability of the proposed gaussian synapse, a prototype chip was fabricated using a 1.2 μm CMOS process and the experimental results obtained are presented.

A Gaussian synapse circuit for analog VLSI neural networks

Back-propagation neural networks with Gaussian function synapses have better convergence property over those with linear-multiplying synapses. In digital simulation, more computing time is spent on Gaussian function evaluation. We present a compact analog synapse cell which is not biased in the subthreshold region for fully-parallel operation. This cell can approximate a Gaussian function with accuracy around 98% in the ideal case. Device mismatch induced by fabrication process will cause some degradation to this approximation. The Gaussian synapse cell can also be used in unsupervised learning. Programmability of the proposed Gaussian synapse cell is achieved by changing the stored synapse weight W/sub ji/, the reference current and the sizes of transistors in the differential pair.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>