scholarly journals An All-in-One Bio-inspired Neural Network

2021 ◽  
Author(s):  
Shiva Subbulakshmi Radhakrishnan ◽  
Akhil Dodda ◽  
Saptarshi Das
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Integrated Circuits ◽  
Layered Materials ◽  
Computing Systems ◽  
Von Neumann ◽  
Monolithically Integrated ◽  
2D Layered Materials ◽  
Learning Challenges ◽  
Biological Learning

Abstract In spite of recent advancements in artificial neural networks (ANNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of the biological neural networks largely remain unimitated in hardware neuromorphic computing systems. Here we exploit optoelectronic, computing, and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS2 to demonstrate a monolithically integrated, multi-pixel, and “all-in-one” bio-inspired neural network (BNN) capable of sensing, encoding, learning, forgetting, and inferring at miniscule energy expenditure. We also demonstrate learning adaptability and stimulate learning challenges under specific synaptic conditions to mimic biological learning. Our findings highlight the potential of in-memory computing and sensing based on emerging 2D materials, devices, and integrated circuits not only to overcome the bottleneck of von Neumann computing in conventional CMOS designs but also aid in eliminating peripheral components necessary for competing technologies such as memristors.

scholarly journals An All-in-One Biomimetic 2D Spiking Neural Network

2021 ◽  
Author(s):  
Shiva Subbulakshmi Radhakrishnan ◽  
Akhil Dodda ◽  
Saptarshi Das
Keyword(s):  
Neural Networks ◽  
Unsupervised Learning ◽  
Bright Light ◽  
Spike Trains ◽  
Spike Timing ◽  
Low Light ◽  
Computing Systems ◽  
Von Neumann ◽  
2D Layered Materials ◽  
Learning Challenges

Abstract In spite of recent advancements in bio-realistic artificial neural networks such as spiking neural networks (SNNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of biological neural networks (BNNs) largely remain unimitated in hardware neuromorphic computing systems. Here we exploit optoelectronic and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS2 to demonstrate an “all-in-one” hardware SNN system which is capable of sensing, encoding, unsupervised learning, and inference at miniscule energy expenditure. In short, we have utilized photogating effect in MoS2 based neuromorphic phototransistor for sensing and direct encoding of analog optical information into graded spike trains, we have designed MoS2 based neuromorphic encoding module for conversion of spike trains into spike-count and spike-timing based programming voltages, and finally we have used arrays of programmable MoS2 non-volatile synapses for spike-based unsupervised learning and inference. We also demonstrate adaptability of our SNN for learning under scotopic (low-light) and photopic (bright-light) conditions mimicking neuroplasticity of BNNs. Furthermore, we use our hardware SNN platform to show learning challenges under specific synaptic conditions, which can aid in understanding learning disabilities in BNNs. Our findings highlight the potential of in-memory computing and sensing based on emerging 2D materials, devices, and circuits not only to overcome the bottleneck of von Neumann computing in conventional CMOS designs but also aid in eliminating peripheral components necessary for competing technologies such as memristors, RRAM, PCM, etc. as well as bridge the understanding between neuroscience of learning and machine learning.

scholarly journals A Neural Network Decomposition Algorithm for Mapping on Crossbar-Based Computing Systems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1526 ◽  
Author(s):  
Choongmin Kim ◽  
Jacob A. Abraham ◽  
Woochul Kang ◽  
Jaeyong Chung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Predictive Performance ◽  
Fixed Number ◽  
Decomposition Algorithm ◽  
Computing Systems ◽  
Network Decomposition ◽  
Von Neumann ◽  
Analog Computing ◽  
The Neural Network

Crossbar-based neuromorphic computing to accelerate neural networks is a popular alternative to conventional von Neumann computing systems. It is also referred as processing-in-memory and in-situ analog computing. The crossbars have a fixed number of synapses per neuron and it is necessary to decompose neurons to map networks onto the crossbars. This paper proposes the k-spare decomposition algorithm that can trade off the predictive performance against the neuron usage during the mapping. The proposed algorithm performs a two-level hierarchical decomposition. In the first global decomposition, it decomposes the neural network such that each crossbar has k spare neurons. These neurons are used to improve the accuracy of the partially mapped network in the subsequent local decomposition. Our experimental results using modern convolutional neural networks show that the proposed method can improve the accuracy substantially within about 10% extra neurons.

scholarly journals Critical Decision Making Using Neural Networks

2018 ◽  
Vol 7 (4.10) ◽  
pp. 15 ◽  
Author(s):  
Rajat Bhati ◽  
Shubham Saraff ◽  
Chhandak Bagchi ◽  
V. Vijayarajan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Emotional Intelligence ◽  
Final Decision ◽  
Computing Systems ◽  
Intelligent Decision Making ◽  
The Neural Network ◽  
Intelligent Decision ◽  
Car Accident

Decision Making influenced by different scenarios is an important feature that needs to be integrated in the computing systems. In this paper, the system takes prompt decisions in emotionally motivated use-cases like in an unavoidable car accident. The system extracts the features from the available visual and processes it in the Neural network. In addition to that the facial recognition plays a key role in returning factors critical to the scenario and hence alter the final decision. Finally, each recognized subject is categorized into six distinct classes which is utilised by the system for intelligent decision-making. Such a system can form the basis of dynamic and intelligent decision-making systems of the future which include elements of emotional intelligence.  

scholarly journals Memristive Spiking Neural Networks Trained with Unsupervised STDP

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 396 ◽  
Author(s):  
Errui Zhou ◽  
Liang Fang ◽  
Binbin Yang
Keyword(s):  
Neural Networks ◽  
Feature Learning ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Learning Method ◽  
Hardware Complexity ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
Von Neumann ◽  
Complex Architectures

Neuromorphic computing systems are promising alternatives in the fields of pattern recognition, image processing, etc. especially when conventional von Neumann architectures face several bottlenecks. Memristors play vital roles in neuromorphic computing systems and are usually used as synaptic devices. Memristive spiking neural networks (MSNNs) are considered to be more efficient and biologically plausible than other systems due to their spike-based working mechanism. In contrast to previous SNNs with complex architectures, we propose a hardware-friendly architecture and an unsupervised spike-timing dependent plasticity (STDP) learning method for MSNNs in this paper. The architecture, which is friendly to hardware implementation, includes an input layer, a feature learning layer and a voting circuit. To reduce hardware complexity, some constraints are enforced: the proposed architecture has no lateral inhibition and is purely feedforward; it uses the voting circuit as a classifier and does not use additional classifiers; all neurons can generate at most one spike and do not need to consider firing rates and refractory periods; all neurons have the same fixed threshold voltage for classification. The presented unsupervised STDP learning method is time-dependent and uses no homeostatic mechanism. The MNIST dataset is used to demonstrate our proposed architecture and learning method. Simulation results show that our proposed architecture with the learning method achieves a classification accuracy of 94.6%, which outperforms other unsupervised SNNs that use time-based encoding schemes.

scholarly journals Implementation of Pruned Backpropagation Neural Network Based on Photonic Integrated Circuits

Photonics ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 363
Author(s):  
Qi Zhang ◽  
Zhuangzhuang Xing ◽  
Duan Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Integrated Circuits ◽  
Energy Efficient ◽  
High Speed ◽  
Large Scale ◽  
Matrix Operation ◽  
Optical Neural Networks ◽  
Optical Neural Network ◽  
Random Initialization

We demonstrate a pruned high-speed and energy-efficient optical backpropagation (BP) neural network. The micro-ring resonator (MRR) banks, as the core of the weight matrix operation, are used for large-scale weighted summation. We find that tuning a pruned MRR weight banks model gives an equivalent performance in training with the model of random initialization. Results show that the overall accuracy of the optical neural network on the MNIST dataset is 93.49% after pruning six-layer MRR weight banks on the condition of low insertion loss. This work is scalable to much more complex networks, such as convolutional neural networks and recurrent neural networks, and provides a potential guide for truly large-scale optical neural networks.

scholarly journals A Neural Network Truth Maintenance System

1991 ◽  
Vol 113 (1) ◽  
pp. 187-191
Author(s):  
W. P. Mounfield ◽  
S. Guddanti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Expert System ◽  
Integrated Circuits ◽  
Limit Cycles ◽  
Experimental Setup ◽  
Maintenance System ◽  
Novel Approach ◽  
Truth Maintenance ◽  
Valid Solution

A novel approach using neural networks to solve expert system problems is presented in this paper. Facts are represented by neurons and their interconnections form the knowledge base. The Truth Maintenance System neural network arrives at a valid solution provided the solution exists. A valid solution is a consistent set of facts. If the solution does not exist the network limit cycles. An experimental setup was built and tested using conventional integrated circuits. The hardware design is suitable for VLSI implementation for large, real-time problems.

scholarly journals A Quantum Approach to Neural Networks

2021 ◽  
pp. 259-299
Author(s):  
Prof. Ahlam Ansari ◽  
Ashhar Shaikh ◽  
Faraz Shaikh ◽  
Faisal Sayed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Training Data ◽  
Feed Forward Neural Network ◽  
Computing Systems ◽  
Quantum Neural Network ◽  
Learning Tasks ◽  
Final Layer ◽  
Quantum Approach ◽  
Universal Quantum

Artificial neural networks, usually just called neural networks, computing systems indefinitely inspired by the biological neural networks and they are extensive in both research as well as industry. It is critical to design quantum Neural Networks for complete quantum learning tasks. In this project, we suggest a computational neural network model based on principles of quantum mechanics which form a quantum feed-forward neural network proficient in universal quantum computation. This structure takes input from one layer of qubits and drives that input onto another layer of qubits. This layer of qubits evaluates this information and drives on the output to the next layer. Eventually, the path leads to the final layer of qubits. The layers do not have to be of the same breadth, meaning they need not have the same number of qubits as the layer before and/or after it. This assembly is trained on which path to take identical to classical ANN. The intended project can be compiled by the subsequent points provided here: 1. The expert training of the quantum neural network utilizing the fidelity as a cost function, providing both conventional and efficient quantum implementations. 2. Use of methods that enable quick optimization with reduced memory requirements. 3. Benchmarking our proposal for the quantum task of learning an unknown unitary and find extraordinary generality and a remarkable sturdiness to noisy training data.

scholarly journals Algorithm for automated visual inspection of MMIC using a classifier based on neural networks

2019 ◽  
Vol 30 ◽  
pp. 04012
Author(s):  
Boris Shiryaev ◽  
Aleksey Bezruk ◽  
Dmitry Argunov ◽  
Aleksey Yushchenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Integrated Circuits ◽  
Visual Inspection ◽  
Automated Visual Inspection ◽  
Classification Result ◽  
Microwave Monolithic Integrated Circuits ◽  
Artificial Neural

We present the algorithm for automated visual inspection of microwave monolithic integrated circuits (MMIC) using computer vision and artificial neural networks. The artificial neural network classifies each pixel of a microphotograph to a certain photomask area. The algorithm detects defectiveness of an MMIC according to classification result and photomask comparison.

scholarly journals Memristive and CMOS Devices for Neuromorphic Computing

Materials ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 166 ◽  
Author(s):  
Valerio Milo ◽  
Gerardo Malavena ◽  
Christian Monzio Compagnoni ◽  
Daniele Ielmini
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Low Power ◽  
Complementary Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Neuromorphic Computing ◽  
Ultra Low Power ◽  
Von Neumann ◽  
Novel Device ◽  
Von Neumann Architecture

Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.

scholarly journals Gaussian synapses for probabilistic neural networks

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Amritanand Sebastian ◽  
Andrew Pannone ◽  
Shiva Subbulakshmi Radhakrishnan ◽  
Saptarshi Das
Keyword(s):  
Neural Networks ◽  
Molybdenum Disulfide ◽  
Field Effect Transistors ◽  
Black Phosphorus ◽  
Probabilistic Neural Networks ◽  
Von Neumann ◽  
2D Layered Materials ◽  
Statistical Neural Networks ◽  
Von Neumann Architecture

Abstract 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.

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