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 videos and links I saved these days in case you are also interested in knowing more about this topic:
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
(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
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
Modeling and Demonstration of Hardware-based Deep Neural Network(DNN) using Memristor Crossbar Array – YouTube – https://www.youtube.com/watch?v=eEYf0110dtw
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