Designing Neuromorphic 3D-Circuits using Memristors

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 –

Three-dimensional hybrid circuits: the future of neuromorphic computing hardware –

(1) (PDF) 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 –

2103.04852.pdf –

Three-Dimensional Neuromorphic Computing System With Two-Layer and Low-Variation Memristive Synapses | IEEE Journals & Magazine | IEEE Xplore –

3D Neuromorphic Hardware with Single Thin‐Film Transistor Synapses Over Single Thin‐Body Transistor Neurons by Monolithic Vertical Integration – PMC –

Fully Integrated Memristor System for Neuromorphic and Analog Computing –

PowerPoint Presentation –

TODAES2501-08 –

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 –

3D neuromorphic circuits | Electrical and Electronic Engineering Community –

Bio‐Inspired 3D Artificial Neuromorphic Circuits – Liu – 2022 – Advanced Functional Materials – Wiley Online Library –

Explore Neuromorphic Computing: Community, Education, and Research –

sandialabs/cross-sim: CrossSim: accuracy simulation of analog in-memory computing –

Microsoft Word – Panzeri_preprint_post.docx –

Journal of Circuits, Systems and Computers –

The Roadmap to Realize Memristive Three-Dimensional Neuromorphic Computing System | IntechOpen –

Materials | Free Full-Text | Memristors for Neuromorphic Circuits and Artificial Intelligence Applications –

ICCAD 2023 – Power-Aware Training for Energy-Efficient Printed Neuromorphic CIrcuits – YouTube –

Energy Efficient AI Hardware: neuromorphic circuits and tools – YouTube –

Machine learning using magnetic stochastic synapses – IOPscience –

memristor explained – YouTube –

Memristor devices ( presentation) – YouTube –

Neuromorphic Computing Explained | Jeffrey Shainline and Lex Fridman – YouTube –

neuromorphic computing – YouTube –

Brain-Like (Neuromorphic) Computing – Computerphile – YouTube –

Bio-inspired Computing with Memristors – YouTube –

A review of memristor: material and structure design, device performance, applications and prospects – PMC –

Frontiers | Overview of Memristor-Based Neural Network Design and Applications –

3D Convolutional Neural Network based on memristor for video recognition – ScienceDirect –

Day-14_Video-1 Neuromorphic Computing by Prof. Shubham Sahay – YouTube –

Flexible Simulation for Neuromorphic Circuit Design: Motion Detection Case Study –

Neuromorphic Hardware –

Handbook of Memristor Networks | SpringerLink –

J. Grollier – Neuromorphic computing: overview and challenges – YouTube –

Ocasys –

Mike Davies – New Tools for a New Era in Neuromorphic Computing – YouTube –

Joshua Yang: Memristive Materials and Devices for Neuromorphic Computing – YouTube –

Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing | July 2023 | Communications of the ACM –

Networking Neuromorphic Engineers – –

Training Spiking Neural Networks Using Lessons From Deep Learning:

10 minutes paper (episode 4); Spiking NN – YouTube –

Cosyne 2022 Tutorial on Spiking Neural Networks – Part 1/2 – YouTube –

Neuromorphic computing with memristors: from device to system – Professor Huaqiang Wu – YouTube –

Neuromorphic: BRAINLIKE Computers – YouTube –

ESWEEK 2021 Education – Spiking Neural Networks – YouTube –

Prof. J. Joshua Yang, Memristive Devices for Neuromorphic Computing – YouTube –

Online dynamical learning and sequence memory with neuromorphic nanowire networks | Nature Communications –

Neuromorphic learning, working memory, and metaplasticity in nanowire networks | Science Advances –

tinyML EMEA 2021 Tutorial: Bio-inspired neuromorphic circuits architectures – YouTube –

Leben und Elektronik – Was kann ein Memristor? – YouTube –

Approaching the Area of Neuromorphic Computing Circuit and System Design | IEEE Conference Publication | IEEE Xplore –

Memristor Discovery (Board, Chip, Software, Manual) – Knowm Inc –

DYNAP™-CNN: Neuromorphic Processor With 1M Spiking neurons –

Old 2010 article about memristor –

On-device machine learning with memristors in the neuromorphic era – YouTube –

Memristor-based Deep Spiking Neural Network with a Computing-In-Memory Architecture – YouTube –

A fully hardware-based memristive multilayer neural network | Science Advances –

Making Memristive Neural Network Accelerators Reliable –

Toolflow for the algorithm-hardware co-design of memristive ANN accelerators – ScienceDirect –

[REFAI Seminar 09/30/21] Circuit Design & Silicon Prototypes for Compute-in-Memory for Deep Learning – YouTube –

Novel Memristor-based Neural Network Accelerators for Space Applications | Activities Portal –

Electronics | Free Full-Text | A Unified and Open LTSPICE Memristor Model Library –


Colloquium: Memristive Neuromorphic Computing Beyond Moore’s Law – YouTube –

Neural Networks and Memristive Hardware Accelerators — Chair of Fundamentals of Electrical Engineering — TU Dresden –

Memristive devices based hardware for unlabeled data processing – IOPscience –

Memristor-based Spiking Neural
Networks.pdf –

21 March 2023 Overview of Neuromorphic Computing Challenges and Opportunities by Sakib Hasan PhD – YouTube –

Text Classification in Memristor-based Spiking
Neural Networks.pdf –

Recent progresses of in-memory computing: materials, devices and architectures – YouTube –

Bill Dally – Trends in Deep Learning Hardware – YouTube –

Training Spiking Neural Networks Using Lessons From Deep Learning – YouTube –

Tutorial on snnTorch: Jason Eshraghian ICONS 2021 – YouTube –

Halide Perovskite Memristors for Neuromorphic Computing and Hardware Security – Rohit Abraham John – YouTube –

Roadmap on Neuromorphic Computing and Engineering – YouTube –

The Basics of Neuromorphic Computing – YouTube –

Spiking Neural Network – SNN – YouTube –

Sensors | Free Full-Text | Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities –

Chinese Scientists Develop Groundbreaking AI Chip Inspired by the Brain – YouTube –

Edge learning using a fully integrated neuro-inspired memristor chip | Science –

Neuromorphic computing with emerging memory devices – YouTube –

‘Mind-blowing’ IBM chip speeds up AI –

Deep Learning Hardware – YouTube –

tinyML Summit 2023: Tiny spiking AI for the sensor-edge – YouTube –

Low-Power Spiking Neural Network Processing Systems for Extreme-Edge Applications – Federico Corradi – YouTube –

In-Memory Computing based Machine Learning Accelerators: Opportunities and Challenges – YouTube –

Why spiking neural networks are important – Simon Thorpe, CERCO – YouTube –

Modeling and Demonstration of Hardware-based Deep Neural Network(DNN) using Memristor Crossbar Array – YouTube –

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