Recently, I discovered a new interesting research area: neuromorphic circuits. Seems like the most interesting electronic circuit component used for designing neuromorphic circuits is the memristor, known as being the 4th electronic circuit component near the resistor, capacitor, and inductor. Its first concept was proposed theoretically in 1971 by Professor Leon Chua at the University of California, Berkeley, and physically realized for the first time only in 2008 by HP.
I remember knowing some ex-colleague of mine from the SRH Hochschule Heidelberg when I was doing an Erasmus semester there in 2013, which was doing some basic experiments with memristors for his Master thesis, but I didn’t take it too seriously at that time, because it was relatively new circuit component, and I was more busy with the software side (web design) that time. Despite some knowledge in AI developed during my Ph.D. and Post-Doc, I was always playing with the software side of AI but never implemented neuromorphic circuits, especially not ones with resistive devices (memristors). These days I was reading some research papers about how one can design some basic neuromorphic circuits using memristors and got a headache. I also installed LTspice, which seems like it is used for simulating simple neuromorphic circuit prototypes (also Cadence Virtuoso I saw being used often). I remember I had it installed in 2013 in Heidelberg, but never since due to me being a non-visionary at that time, haha.
A guy who does research in this field told me „For the circuits, I use LTSPice with compact models of memristors for prototyping. And then I use Cadence Analog mixed signals design toolchain to use the PDK and then transfer and fine-tune the transistor size etc.“ Also, „in order to stack them vertically, seems like if you want to have a working prototype, currently the only option is the Skywater 130nm process from Google„. Oh, Tesla, where are you?! I cry inside.
Below, I will post some links (I will add more links soon) in case you are also interested in knowing more about this topic:
3D integration of 2D electronics – https://www.nature.com/articles/s44287-024-00038-5
Hardware implementation of memristor-based artificial neural networks – https://www.nature.com/articles/s41467-024-45670-9
Memristors and Memelements – https://link.springer.com/book/10.1007/978-3-031-25625-7
Neuromorphic 3D-Circuits – Google Suche – https://www.google.com/search?q=Neuromorphic+3D-Circuits&rlz=1C1CHBF_enDE1035DE1035&sourceid=chrome&ie=UTF-8#ip=1
Three-dimensional hybrid circuits: the future of neuromorphic computing hardware – https://iopscience.iop.org/article/10.1088/2632-959X/ac280e
Three-dimensional memristor circuits as complex neural networks – https://www.nature.com/articles/s41928-020-0397-9
(1) (PDF) Neuromorphic 3D Integrated Circuit: A Hybrid, Reliable and Energy Efficient Approach for Next Generation Computing – https://www.researchgate.net/publication/317051142_Neuromorphic_3D_Integrated_Circuit_A_Hybrid_Reliable_and_Energy_Efficient_Approach_for_Next_Generation_Computing
Monolithic 3D neuromorphic computing system with hybrid CMOS and memristor-based synapses and neurons – ScienceDirect – https://www.sciencedirect.com/science/article/abs/pii/S0167926017303413
2103.04852.pdf – https://arxiv.org/ftp/arxiv/papers/2103/2103.04852.pdf
Three-Dimensional Neuromorphic Computing System With Two-Layer and Low-Variation Memristive Synapses | IEEE Journals & Magazine | IEEE Xplore – https://ieeexplore.ieee.org/document/9360870
3D Neuromorphic Hardware with Single Thin‐Film Transistor Synapses Over Single Thin‐Body Transistor Neurons by Monolithic Vertical Integration – PMC – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602577/
Fully Integrated Memristor System for Neuromorphic and Analog Computing – https://apps.dtic.mil/sti/trecms/pdf/AD1171350.pdf
PowerPoint Presentation – https://www.mics.ece.vt.edu/content/dam/mics_ece_vt_edu/People/report/QE_Slide_HYA.pdf
TODAES2501-08 – https://www.cse.cuhk.edu.hk/~byu/papers/J42-TODAES2019-3D-FCN.pdf
3D-Integrated Neuromorphic Hardware With A Two-Level Neuromorphic “Synapse Over Neuron” Structure – https://semiengineering.com/3d-integrated-neuromorphic-hardware-with-a-two-level-neuromorphic-synapse-over-neuron-structure/
Brain-derived neuromorphic computing with 3D electronic-photonic integrated circuits| Yoo | Publications | Spie – https://spie.org/Publications/Proceedings/Paper/10.1117/12.2651109?SSO=1
3D neuromorphic circuits | Electrical and Electronic Engineering Community – https://engineeringcommunity.nature.com/posts/64163-3d-neuromorphic-circuits
Bio‐Inspired 3D Artificial Neuromorphic Circuits – Liu – 2022 – Advanced Functional Materials – Wiley Online Library – https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.202113050
Explore Neuromorphic Computing: Community, Education, and Research – https://open-neuromorphic.org/
sandialabs/cross-sim: CrossSim: accuracy simulation of analog in-memory computing – https://github.com/sandialabs/cross-sim
Microsoft Word – Panzeri_preprint_post.docx – https://arxiv.org/ftp/arxiv/papers/2212/2212.04317.pdf
Journal of Circuits, Systems and Computers – https://www.worldscientific.com/doi/pdf/10.1142/S0218126624501007?download=true
The Roadmap to Realize Memristive Three-Dimensional Neuromorphic Computing System | IntechOpen – https://www.intechopen.com/chapters/62198
Materials | Free Full-Text | Memristors for Neuromorphic Circuits and Artificial Intelligence Applications – https://www.mdpi.com/1996-1944/13/4/938
ICCAD 2023 – Power-Aware Training for Energy-Efficient Printed Neuromorphic CIrcuits – YouTube – https://www.youtube.com/watch?v=w-LW_8nwX9M
Energy Efficient AI Hardware: neuromorphic circuits and tools – YouTube – https://www.youtube.com/watch?v=PT0TO2pTrMo
Machine learning using magnetic stochastic synapses – IOPscience – https://iopscience.iop.org/article/10.1088/2634-4386/acdb96
memristor explained – YouTube – https://www.youtube.com/results?search_query=memristor+explained
Memristor devices ( presentation) – YouTube – https://www.youtube.com/watch?v=qAS0uCb6JxE
Neuromorphic Computing Explained | Jeffrey Shainline and Lex Fridman – YouTube – https://www.youtube.com/watch?v=u22-2CTErIQ
neuromorphic computing – YouTube – https://www.youtube.com/results?search_query=neuromorphic+computing
Brain-Like (Neuromorphic) Computing – Computerphile – YouTube – https://www.youtube.com/watch?v=Qow8pIvExH4
Bio-inspired Computing with Memristors – YouTube – https://www.youtube.com/watch?v=P2NO2lJyhkk
A review of memristor: material and structure design, device performance, applications and prospects – PMC – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980037/
Frontiers | Overview of Memristor-Based Neural Network Design and Applications – https://www.frontiersin.org/articles/10.3389/fphy.2022.839243/full
3D Convolutional Neural Network based on memristor for video recognition – ScienceDirect – https://www.sciencedirect.com/science/article/abs/pii/S0167865518309164
Day-14_Video-1 Neuromorphic Computing by Prof. Shubham Sahay – YouTube – https://www.youtube.com/watch?v=C2PYrPIPhQY
Flexible Simulation for Neuromorphic Circuit Design: Motion Detection Case Study – https://hal.science/hal-01538449/document
Neuromorphic Hardware – https://www.iis.fraunhofer.de/en/ff/kom/ai/neuromorphic.html
Handbook of Memristor Networks | SpringerLink – https://link.springer.com/book/10.1007/978-3-319-76375-0
J. Grollier – Neuromorphic computing: overview and challenges – YouTube – https://www.youtube.com/watch?v=yfnJqVHmJEw
Ocasys – https://ocasys.rug.nl/current/catalog/course/WMPH044-05
Mike Davies – New Tools for a New Era in Neuromorphic Computing – YouTube – https://www.youtube.com/watch?v=Fnf9yewGg1w
Joshua Yang: Memristive Materials and Devices for Neuromorphic Computing – YouTube – https://www.youtube.com/watch?v=4CI9Z0DM-AE
Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing | July 2023 | Communications of the ACM – https://cacm.acm.org/magazines/2023/7/274054-achieving-green-ai-with-energy-efficient-deep-learning-using-neuromorphic-computing/fulltext
Networking Neuromorphic Engineers – – https://www.neuropac.info/
Training Spiking Neural Networks Using Lessons From Deep Learning: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10242251
10 minutes paper (episode 4); Spiking NN – YouTube – https://www.youtube.com/watch?v=9dYZXQl4ozk
Cosyne 2022 Tutorial on Spiking Neural Networks – Part 1/2 – YouTube – https://www.youtube.com/watch?v=GTXTQ_sOxak
Neuromorphic computing with memristors: from device to system – Professor Huaqiang Wu – YouTube – https://www.youtube.com/watch?v=yChwSOXO538
Neuromorphic: BRAINLIKE Computers – YouTube – https://www.youtube.com/watch?v=19fjsk9blB4
ESWEEK 2021 Education – Spiking Neural Networks – YouTube – https://www.youtube.com/watch?v=7TybETlCslM
Prof. J. Joshua Yang, Memristive Devices for Neuromorphic Computing – YouTube – https://www.youtube.com/watch?v=35vu8qFy3Ys
Online dynamical learning and sequence memory with neuromorphic nanowire networks | Nature Communications – https://www.nature.com/articles/s41467-023-42470-5?
Neuromorphic learning, working memory, and metaplasticity in nanowire networks | Science Advances – https://www.science.org/doi/full/10.1126/sciadv.adg3289
tinyML EMEA 2021 Tutorial: Bio-inspired neuromorphic circuits architectures – YouTube – https://www.youtube.com/watch?v=aHHlBFqS99Y
Leben und Elektronik – Was kann ein Memristor? – YouTube – https://www.youtube.com/watch?v=sp9IZpAe8X0
Approaching the Area of Neuromorphic Computing Circuit and System Design | IEEE Conference Publication | IEEE Xplore – https://ieeexplore.ieee.org/document/9651627
Memristor Discovery (Board, Chip, Software, Manual) – Knowm Inc – https://knowm.com/collections/frontpage/products/memristor-discovery-board-chip-manual
DYNAP™-CNN: Neuromorphic Processor With 1M Spiking neurons – https://www.synsense.ai/products/dynap-cnn/
Old 2010 article about memristor – https://arxiv.org/ftp/arxiv/papers/1008/1008.2836.pdf
On-device machine learning with memristors in the neuromorphic era – YouTube – https://www.youtube.com/watch?v=bjcJg-bwBkU
Memristor-based Deep Spiking Neural Network with a Computing-In-Memory Architecture – YouTube – https://www.youtube.com/watch?v=hmsRDjk2lX0
A fully hardware-based memristive multilayer neural network | Science Advances – https://www.science.org/doi/10.1126/sciadv.abj4801
Making Memristive Neural Network Accelerators Reliable – https://www.bfeinberg.com/hpca18.pdf
Toolflow for the algorithm-hardware co-design of memristive ANN accelerators – ScienceDirect – https://www.sciencedirect.com/science/article/pii/S2773064623000439
[REFAI Seminar 09/30/21] Circuit Design & Silicon Prototypes for Compute-in-Memory for Deep Learning – YouTube – https://www.youtube.com/watch?v=vBvBiCZC2tw
Novel Memristor-based Neural Network Accelerators for Space Applications | Activities Portal – https://activities.esa.int/4000140774
Electronics | Free Full-Text | A Unified and Open LTSPICE Memristor Model Library – https://www.mdpi.com/2079-9292/10/13/1594
HOW NEUROMORPHIC COMPUTING WILL ACCELERATE ARTIFICIAL INTELLIGENCE – PROF SHUBHAM SAHAY- IIT KANPUR – YouTube – https://www.youtube.com/watch?v=sMjkG0jGCBs
Colloquium: Memristive Neuromorphic Computing Beyond Moore’s Law – YouTube – https://www.youtube.com/watch?v=DK_gzUujnXo
Neural Networks and Memristive Hardware Accelerators — Chair of Fundamentals of Electrical Engineering — TU Dresden – https://tu-dresden.de/ing/elektrotechnik/iee/ge/studium/lehrveranstaltungen/neural-networks-and-memristive-hardware-accelerators
Memristive devices based hardware for unlabeled data processing – IOPscience – https://iopscience.iop.org/article/10.1088/2634-4386/ac734a/meta
Memristor-based Spiking Neural
Networks.pdf – https://eprints.soton.ac.uk/471765/1/PhD_Thesis_1_.pdf
21 March 2023 Overview of Neuromorphic Computing Challenges and Opportunities by Sakib Hasan PhD – YouTube – https://www.youtube.com/watch?v=fSOpdgQXIao
Text Classification in Memristor-based Spiking
Neural Networks.pdf – https://arxiv.org/pdf/2207.13729.pdf and its code implementation on GitHub: text-classification-in-memristorsnn
Recent progresses of in-memory computing: materials, devices and architectures – YouTube – https://www.youtube.com/watch?v=V3AQytpXI_s
Bill Dally – Trends in Deep Learning Hardware – YouTube – https://www.youtube.com/watch?v=4F_vfPFNe04
Training Spiking Neural Networks Using Lessons From Deep Learning – YouTube – https://www.youtube.com/watch?v=zldal7b7sJ4
Tutorial on snnTorch: Jason Eshraghian ICONS 2021 – YouTube – https://www.youtube.com/watch?v=O2-mT291ygg
Halide Perovskite Memristors for Neuromorphic Computing and Hardware Security – Rohit Abraham John – YouTube – https://www.youtube.com/watch?v=umaosrVFwk4
Roadmap on Neuromorphic Computing and Engineering – YouTube – https://www.youtube.com/watch?v=cR8I39rUWyM
The Basics of Neuromorphic Computing – YouTube – https://www.youtube.com/watch?v=o59pL5O4mXY
Spiking Neural Network – SNN – YouTube – https://www.youtube.com/watch?v=CJ_x9jYY4JU
Sensors | Free Full-Text | Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities – https://www.mdpi.com/1424-8220/23/6/3037
Chinese Scientists Develop Groundbreaking AI Chip Inspired by the Brain – YouTube – https://www.youtube.com/watch?v=Ae6Eel7atXA
Edge learning using a fully integrated neuro-inspired memristor chip | Science – https://www.science.org/doi/10.1126/science.ade3483
Neuromorphic computing with emerging memory devices – YouTube – https://www.youtube.com/watch?v=gX9NqDuwTnA
‘Mind-blowing’ IBM chip speeds up AI – https://www.nature.com/articles/d41586-023-03267-0
Deep Learning Hardware – YouTube – https://www.youtube.com/watch?v=AGcv_PRKrPQ
tinyML Summit 2023: Tiny spiking AI for the sensor-edge – YouTube – https://www.youtube.com/watch?v=p9n6Pi46wT0
Low-Power Spiking Neural Network Processing Systems for Extreme-Edge Applications – Federico Corradi – YouTube – https://www.youtube.com/watch?v=xiYUVzdwDIA
In-Memory Computing based Machine Learning Accelerators: Opportunities and Challenges – YouTube – https://www.youtube.com/watch?v=TP2LVkgWCVE
Why spiking neural networks are important – Simon Thorpe, CERCO – YouTube – https://www.youtube.com/watch?v=8K5oc4y0Vas
Small Molecule Memristors for Neuromorphic Computing by Aaron Cookson – https://www.youtube.com/watch?v=nKdpfKcQ7EI
Modeling and Demonstration of Hardware-based Deep Neural Network(DNN) using Memristor Crossbar Array – YouTube – https://www.youtube.com/watch?v=eEYf0110dtw
Memristors for Neuromorphic Circuits and Artificial Intelligence Applications – https://www.mdpi.com/books/book/2171-memristors-for-neuromorphic-circuits-and-artificial-intelligence-applications
Fundamentals of spiking neural networks and weight optimization – https://www.youtube.com/watch?v=HATzzSM9Epg
Highly-Bespoke Robust Printed Neuromorphic Circuits – https://publikationen.bibliothek.kit.edu/1000156490
Aging-Aware Training for Printed Neuromorphic Circuits – https://publikationen.bibliothek.kit.edu/1000150219
Neuromorphic Circuits – A constructive approach – https://iopscience.iop.org/book/edit/978-0-7503-5097-6
Expressivity of Spiking Neural Networks – https://arxiv.org/pdf/2308.08218.pdf
European Solid-State Electronics Research Conference – https://www.esserc2024.org/
Neuromorphic Computing Group – Brain-Inspired Systems at UC Santa Cruz – https://ncg.ucsc.edu/category/presentations/
MemrisTec Summer School 2023 – https://memristec.de/summerschool-2023/
Neuromorphic Circuits and Systems: From Neuron Models to Integrate-and-Fire Arrays – https://link.springer.com/referenceworkentry/10.1007/978-981-16-5540-1_42
Neural Network Scalable Spiking Simulator (N2S3) – https://sourcesup.renater.fr/wiki/n2s3/
Research Progress of Neural Synapses Based on Memristors – https://www.mdpi.com/2079-9292/12/15/3298
Wei Lu (U Mich) Neuromorphic Computing Based on Memristive Materials and Devices – https://www.youtube.com/watch?v=qfBlqCZdG6U
Memristor Fabrication Through Printing Technologies: A Review – https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9469791
Neuromorphic Computing with Memristors: From Devices to Integrated Systems – https://deepblue.lib.umich.edu/handle/2027.42/149957
Integrating dimensions to get more out of Moore’s Law and advance electronics – https://www.psu.edu/news/materials-research-institute/story/integrating-dimensions-get-more-out-moores-law-and-advance/
3D-STACKED CMOS TAKES MOORE’S LAW TO NEW HEIGHTS – https://spectrum.ieee.org/3d-cmos
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks – https://ieeexplore.ieee.org/document/9311226
Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems – https://www.nature.com/articles/s41467-023-44365-x
Brain organoid reservoir computing for artificial intelligence – https://www.nature.com/articles/s41928-023-01069-w
Memristor-based spiking neural network with online reinforcement learning – https://www.sciencedirect.com/science/article/abs/pii/S0893608023003891
A Fully Memristive Spiking Neural Network with Unsupervised Learning – https://arxiv.org/pdf/2203.01416.pdf
Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update – https://www.frontiersin.org/articles/10.3389/fncom.2021.646125/full
SNNSim: Investigation and Optimization of Large‐Scale Analog Spiking Neural Networks Based on Flash Memory Devices – https://onlinelibrary.wiley.com/doi/10.1002/aisy.202300456
Memristor‐Based Neuromorphic Chips – https://www.researchgate.net/publication/377085394_Memristor-Based_Neuromorphic_Chips
Lesson 2: The neuron and nervous system – https://www.khanacademy.org/science/biology/human-biology/neuron-nervous-system/a/the-synapse
Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators – https://www.nature.com/articles/s41467-023-44620-1
Fully hardware-implemented memristor convolutional neural network – https://www.nature.com/articles/s41586-020-1942-4
Efficient Structure Slimming for Spiking Neural Networks – https://www.researchgate.net/publication/377344840_Efficient_Structure_Slimming_for_Spiking_Neural_Networks
An overview memristor based hardware accelerators for deep neural network – https://onlinelibrary.wiley.com/doi/10.1002/cpe.7997
An integrate-and-fire neuron circuit made from printed organic field-effect transistors – https://www.sciencedirect.com/science/article/pii/S1566119922002579
A Case for 3D Integrated System Design for Neuromorphic Computing & AI Applications – https://arxiv.org/ftp/arxiv/papers/2103/2103.04852.pdf
Neuromorphic computing based on halide perovskites – https://www.nature.com/articles/s41928-023-01082-z
Memristors for Neuromorphic Circuits and Artificial Intelligence Applications – https://ddd.uab.cat/pub/artpub/2020/241132/Mat_a2020v13n4p938.pdf
NeuroPack: An Algorithm-level Python-based Simulator for Memristor-empowered Neuro-inspired Computing – https://www.researchgate.net/publication/357735288_NeuroPack_An_Algorithm-level_Python-based_Simulator_for_Memristor-empowered_Neuro-inspired_Computing
A pseudo-memcapacitive neurotransistor for spiking neural networks – https://tu-dresden.de/ing/elektrotechnik/iee/ge/ressourcen/dateien/postprints/2023-Schroedter-A_pseudo-memcapacitive_neurotransistor_for_spiking_neural_networks.pdf?lang=en
Revisiting the memristor concept within basic circuit theory – https://onlinelibrary.wiley.com/doi/abs/10.1002/cta.3111
Memristors Run AI Tasks at 1/800th Power – https://spectrum.ieee.org/memristor-devices-ai
Review on the Basic Circuit Elements and Memristor Interpretation: Analysis, Technology and Applications – https://www.mdpi.com/2079-9268/12/3/44
Hybrid 2D–CMOS microchips for memristive applications – https://www.nature.com/articles/s41586-023-05973-1
Three-dimensional integrated circuit using printed electronics – https://www.sciencedirect.com/science/article/abs/pii/S1566119910004088
CAREER: Enabling Brian-like Computing through 3D Neuromorphic Circuits and Systems – https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750450&HistoricalAwards=false
Enabling Neuromorphic Computing for Artificial Intelligence with Hardware-Software Co-Design – https://www.intechopen.com/chapters/87382
The improbable but highly appropriate marriage of 3D stacking and neuromorphic accelerators – https://www.academia.edu/80585188/The_improbable_but_highly_appropriate_marriage_of_3D_stacking_and_neuromorphic_accelerators
SkyNet: Memristor-based 3D IC for artificial neural networks – https://www.academia.edu/99618036/SkyNet_Memristor_based_3D_IC_for_artificial_neural_networks?uc-g-sw=80585188
Neuromorphic Hardware and Computing – https://www.nature.com/collections/jaidjgeceb
Modeling and Design of a 3D Interconnect Based Circuit Cell Formed with 3D SiP Techniques Mimicking Brain Neurons for Neuromorphic Computing Applications – https://ieeexplore.ieee.org/document/8429591
Modeling and Analysis of CMOS-based Folded Memristive Crossbar Array for 3D Neuromorphic Integrated Circuits – https://www.researchgate.net/publication/372897084_Modeling_and_Analysis_of_CMOS-based_Folded_Memristive_Crossbar_Array_for_3D_Neuromorphic_Integrated_Circuits
Neuromorphic 3D Integrated Circuit: A Hybrid, Reliable and Energy Efficient Approach for Next Generation Computing – https://www.researchgate.net/publication/317051142_Neuromorphic_3D_Integrated_Circuit_A_Hybrid_Reliable_and_Energy_Efficient_Approach_for_Next_Generation_Computing
Electrical modeling and analysis of sidewall roughness of through silicon vias in 3D integration – https://www.researchgate.net/publication/288818680_Electrical_modeling_and_analysis_of_sidewall_roughness_of_through_silicon_vias_in_3D_integration
A Novel Approach for using TSVs as Membrane Capacitance in Neuromorphic 3D IC – https://www.researchgate.net/publication/320261446_A_Novel_Approach_for_using_TSVs_as_Membrane_Capacitance_in_Neuromorphic_3D_IC
Monolithic 3D neuromorphic computing system with hybrid CMOS and memristor-based synapses and neurons – https://www.researchgate.net/publication/320773162_Monolithic_3D_neuromorphic_computing_system_with_hybrid_CMOS_and_memristor-based_synapses_and_neurons
Electrical modeling and analysis of 3D synaptic array using vertical RRAM structure – https://www.researchgate.net/publication/316727654_Electrical_modeling_and_analysis_of_3D_synaptic_array_using_vertical_RRAM_structure
Design and simulation of memristor-based neural networks – https://arxiv.org/abs/2306.11678
Research Progress of Neural Synapses Based on Memristors – https://www.mdpi.com/2079-9292/12/15/3298
Inkjet-Printed High-Yield, Reconfigurable, and Recyclable Memristors on Paper – https://arxiv.org/abs/2312.16501
Inkjet-Printed Tungsten Oxide Memristor Displaying Non-Volatile Memory and Neuromorphic Properties – https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202302290
Artificial Neurons on Flexible Substrates: A Fully Printed Approach for Neuromorphic Sensing – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182789/
Python-Based Circuit Design for Fundamental Building Blocks of Spiking Neural Network – https://www.mdpi.com/2079-9292/12/11/2351
CMOS Circuit Implementation of Spiking Neural Network for Pattern Recognition Using On-chip Unsupervised STDP Learning – https://arxiv.org/pdf/2204.04430.pdf
Circuit implementation of on-chip trainable spiking neural network using CMOS based memristive STDP synapses and LIF neurons – https://www.sciencedirect.com/science/article/abs/pii/S0167926023001645
Full CMOS Circuit for Brain-Inspired Associative Memory With On-Chip Trainable Memristive STDP Synapse – https://www.researchgate.net/publication/370451314_Full_CMOS_Circuit_for_Brain-Inspired_Associative_Memory_With_On-Chip_Trainable_Memristive_STDP_Synapse
Design and implementation of training in hardware for efficient artificial intelligence – https://pure.tue.nl/ws/portalfiles/portal/175402550/1297155_PranshuShubham_TUE_MEP_Report_2021.pdf
A Three-dimensional (3D) Memristive Spiking Neural Network (M-SNN) System – https://ieeexplore.ieee.org/document/9424303
A Novel Memristive Neural Network Circuit and Its Application in Character Recognition – https://www.mdpi.com/2072-666X/13/12/2074
Memristor-Based Neural Network Implementation with Adjustable Synaptic Weights in LTSPICE – https://ieeexplore.ieee.org/document/10339092
A Memristor Neural Network Based on Simple Logarithmic-Sigmoidal Transfer Function with MOS Transistors – https://www.preprints.org/manuscript/202401.1245/v1
SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning – https://arxiv.org/abs/2401.04491
Programming memristor arrays with arbitrarily high precision for analog computing – https://www.science.org/doi/10.1126/science.adi9405
Nanodevices and Integrated Systems Laboratory at UMass Amherst – http://nano.ecs.umass.edu/
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