scholarly journals Nanoparticle-based computing architecture for nanoparticle neural networks

2020 ◽  
Vol 6 (35) ◽  
pp. eabb3348
Author(s):  
Sungi Kim ◽  
Namjun Kim ◽  
Jinyoung Seo ◽  
Jeong-Eun Park ◽  
Eun Ho Song ◽  
...  
Keyword(s):  
Neural Networks ◽  
Boolean Logic ◽  
Circuit Board ◽  
Single Chip ◽  
Feed Forward Neural Network ◽  
Computing Architecture ◽  
Von Neumann ◽  
Dna Strands ◽  
Logic Operations ◽  
Von Neumann Architecture

The lack of a scalable nanoparticle-based computing architecture severely limits the potential and use of nanoparticles for manipulating and processing information with molecular computing schemes. Inspired by the von Neumann architecture (VNA), in which multiple programs can be operated without restructuring the computer, we realized the nanoparticle-based VNA (NVNA) on a lipid chip for multiple executions of arbitrary molecular logic operations in the single chip without refabrication. In this system, nanoparticles on a lipid chip function as the hardware that features memory, processors, and output units, and DNA strands are used as the software to provide molecular instructions for the facile programming of logic circuits. NVNA enables a group of nanoparticles to form a feed-forward neural network, a perceptron, which implements functionally complete Boolean logic operations, and provides a programmable, resettable, scalable computing architecture and circuit board to form nanoparticle neural networks and make logical decisions.

scholarly journals A 0.94 μW 611 KHz In-Situ Logic Operation in Embedded DRAM Memory Arrays in 90 nm CMOS

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 865
Author(s):  
Myeong-Eun Hwang ◽  
Sungoh Kwon
Keyword(s):  
Reducing Power ◽  
Data Traffic ◽  
Von Neumann ◽  
Memory Array ◽  
Process Measurement ◽  
Measurement Results ◽  
Logic Operations ◽  
Von Neumann Architecture ◽  
Time And Energy

Conventional computers based on the Von Neumann architecture conduct computation with repeated data movements between their separate processing and memory units, where each movement takes time and energy. Unlike this approach, we experimentally study memory that can perform computation as well as store data within a generic memory array in a non-Von Neumann architecture way. Memory array can innately perform NOR operation that is functionally complete and thus realize any Boolean functions like inversion (NOT), disjunction (OR) and conjunction (AND) operations. With theoretical exploration of memory array performing Boolean computation along with storing data, we demonstrate another potential of memory array with a test chip fabricated in a 90 nm logic process. Measurement results confirm valid in-situ memory logic operations in a 32-kbit memory system that successfully operates down to 135 mV consuming 130 nW at 750 Hz, reducing power and data traffic between the units by five orders of magnitude at the sacrifice of performance.

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 Spiking Neural Networks in Spintronic Computational RAM

2021 ◽  
Vol 18 (4) ◽  
pp. 1-21
Author(s):  
Hüsrev Cılasun ◽  
Salonik Resch ◽  
Zamshed I. Chowdhury ◽  
Erin Olson ◽  
Masoud Zabihi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Data Transfer ◽  
Data Communication ◽  
Spiking Neural Networks ◽  
De Bruijn Graph ◽  
Promising Alternative ◽  
Von Neumann ◽  
Key Aspects ◽  
Von Neumann Architecture

Spiking Neural Networks (SNNs) represent a biologically inspired computation model capable of emulating neural computation in human brain and brain-like structures. The main promise is very low energy consumption. Classic Von Neumann architecture based SNN accelerators in hardware, however, often fall short of addressing demanding computation and data transfer requirements efficiently at scale. In this article, we propose a promising alternative to overcome scalability limitations, based on a network of in-memory SNN accelerators, which can reduce the energy consumption by up to 150.25= when compared to a representative ASIC solution. The significant reduction in energy comes from two key aspects of the hardware design to minimize data communication overheads: (1) each node represents an in-memory SNN accelerator based on a spintronic Computational RAM array, and (2) a novel, De Bruijn graph based architecture establishes the SNN array connectivity.

scholarly journals Nano-bio-computing lipid nanotablet

2019 ◽  
Vol 5 (2) ◽  
pp. eaau2124 ◽  
Author(s):  
Jinyoung Seo ◽  
Sungi Kim ◽  
Ha H. Park ◽  
Da Yeon Choi ◽  
Jwa-Min Nam
Keyword(s):  
Lipid Bilayer ◽  
Logic Gate ◽  
Logic Gates ◽  
Boolean Logic ◽  
Circuit Board ◽  
Particle Assembly ◽  
Supported Lipid Bilayer ◽  
Particle Level ◽  
Logic Operations ◽  
And Control

Using nanoparticles as substrates for computation enables algorithmic and autonomous controls of their unique and beneficial properties. However, scalable architecture for nanoparticle-based computing systems is lacking. Here, we report a platform for constructing nanoparticle logic gates and circuits at the single-particle level on a supported lipid bilayer. Our “lipid nanotablet” platform, inspired by cellular membranes that are exploited to compartmentalize and control signaling networks, uses a lipid bilayer as a chemical circuit board and nanoparticles as computational units. On a lipid nanotablet, a single-nanoparticle logic gate senses molecules in solution as inputs and triggers particle assembly or disassembly as an output. We demonstrate a set of Boolean logic operations, fan-in/fan-out of logic gates, and a combinational logic circuit such as a multiplexer. We envisage that our approach to modularly implement nanoparticle circuits on a lipid bilayer will create new paradigms and opportunities in molecular computing, nanoparticle circuits, and systems nanoscience.

Solution-processed electronics for artificial synapses

2021 ◽  
Author(s):  
Kuakua Lu ◽  
Xiaomeng Li ◽  
Qingqing Sun ◽  
Xinchang Pang ◽  
Jinzhou Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Three Dimensional ◽  
Solution Processed ◽  
Computing Architecture ◽  
Von Neumann

Solution-processed artificial synapses are expected to develop the synaptic electronics towards flexible and highly integrated three-dimensional neural networks to break through the von Neumann computing architecture in the post-Moore era.

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.

scholarly journals Digital logic gates in soft, conductive mechanical metamaterials

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Charles El Helou ◽  
Philip R. Buskohl ◽  
Christopher E. Tabor ◽  
Ryan L. Harne
Keyword(s):  
Integrated Circuits ◽  
Electronic Materials ◽  
Polymer Networks ◽  
Conductive Polymer ◽  
Network Connectivity ◽  
Logic Gates ◽  
Boolean Logic ◽  
Digital Logic ◽  
Mechanical Metamaterials ◽  
Logic Operations

AbstractIntegrated circuits utilize networked logic gates to compute Boolean logic operations that are the foundation of modern computation and electronics. With the emergence of flexible electronic materials and devices, an opportunity exists to formulate digital logic from compliant, conductive materials. Here, we introduce a general method of leveraging cellular, mechanical metamaterials composed of conductive polymers to realize all digital logic gates and gate assemblies. We establish a method for applying conductive polymer networks to metamaterial constituents and correlate mechanical buckling modes with network connectivity. With this foundation, each of the conventional logic gates is realized in an equivalent mechanical metamaterial, leading to soft, conductive matter that thinks about applied mechanical stress. These findings may advance the growing fields of soft robotics and smart mechanical matter, and may be leveraged across length scales and physics.

scholarly journals Symmetry perception with spiking neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonathan K. George ◽  
Cesare Soci ◽  
Mario Miscuglio ◽  
Volker J. Sorger
Keyword(s):  
Neural Networks ◽  
Mirror Symmetry ◽  
Satellite Image ◽  
Image Data ◽  
Coincidence Detection ◽  
Spiking Neural Networks ◽  
Feed Forward Neural Network ◽  
Neuromorphic Computing ◽  
Symmetry Perception ◽  
High Level

AbstractMirror symmetry is an abundant feature in both nature and technology. Its successful detection is critical for perception procedures based on visual stimuli and requires organizational processes. Neuromorphic computing, utilizing brain-mimicked networks, could be a technology-solution providing such perceptual organization functionality, and furthermore has made tremendous advances in computing efficiency by applying a spiking model of information. Spiking models inherently maximize efficiency in noisy environments by placing the energy of the signal in a minimal time. However, many neuromorphic computing models ignore time delay between nodes, choosing instead to approximate connections between neurons as instantaneous weighting. With this assumption, many complex time interactions of spiking neurons are lost. Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. We further demonstrate that the addition of noise improves feature detectability of an image through coincidence point generation. The ability to obtain mirror symmetry from spiking neural networks can be a powerful tool for applications in image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media and may help accelerating the brain-machine interconnection. More importantly it enables a technology pathway in bridging the gap between the low-level incoming sensor stimuli and high-level interpretation of these inputs as recognized objects and scenes in the world.

scholarly journals Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1101
Author(s):  
Elena M. Tosca ◽  
Roberta Bartolucci ◽  
Paolo Magni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Molecular Descriptor ◽  
Biological Properties ◽  
Training Dataset ◽  
Structure Property ◽  
Feed Forward Neural Network ◽  
Qspr Model ◽  
Artificial Neural ◽  
Intrinsic Solubility

Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (logS0) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with logS0 value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict logS0; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.

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