ArticleNature communications2019
Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Large-scale integrated optoelectronic chaos for machine learning acceleration.Nature communications · 2026Article
- Harnessing the full potential of RRAMs through scalable and distributed in-memory computing with integrated error correction.Communications engineering · 2026Article
- Resistive Switching Random-Access Memory (RRAM): Applications and Requirements for Memory and Computing.Chemical reviews · 2025Review
- Memristive Bellman solver for decision-making.Nature communications · 2025Article
- A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Solving Max-Cut Problem Using Spiking Boltzmann Machine Based on Neuromorphic Hardware with Phase Change Memory.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Computing high-degree polynomial gradients in memory.Nature communications · 2024Article
- Unique Coexistence of Two Resistive Switching Modes in a Memristor Device Enables Multifunctional Neuromorphic Computing Properties.ACS applied materials & interfaces · 2024Article
- Efficient combinatorial optimization by quantum-inspired parallel annealing in analogue memristor crossbar.Nature communications · 2023Article
- A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing.Nature communications · 2023Article
- Research progress in architecture and application of RRAM with computing-in-memory.Nanoscale advances · 2023Review
- Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network.Sensors (Basel, Switzerland) · 2023Review
- Large-scale coherent Ising machine based on optoelectronic parametric oscillator.Light, science & applications · 2022Article
- Probabilistic computing using CuNature communications · 2022Article
- Toward memristive in-memory computing: principles and applications.Frontiers of optoelectronics · 2022Review
- Applications and Techniques for Fast Machine Learning in Science.Frontiers in big data · 2022Review
- Nonlinearity in Memristors for Neuromorphic Dynamic Systems.Small science · 2022Article
- Inherent stochasticity during insulator-metal transition in VOProceedings of the National Academy of Sciences of the United States of America · 2021Article
- Combinatorial optimization by weight annealing in memristive hopfield networks.Scientific reports · 2021Article
- Novel Program Scheme of Vertical NAND Flash Memory for Reduction of Z-Interference.Micromachines · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The circuit's high performance is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit's noise, intrinsic and/or extrinsic to the memory cell array. The dynamic scaling of weights, enabled by analog memory devices, allows for efficient realization of different annealing approaches to improve functionality. The proposed approach is experimentally verified for two representative applications, namely by implementing neural network for solving a four-node graph-partitioning problem, and a Boltzmann machine with 10-input and 8-hidden neurons.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.