("Theory day")

Agenda as .pdf download

The agenda as of 11 April 2023 can be downloaded here as .pdf.

Venue

UTSA Student Union, H-E-B University Center, 1 UTSA Circle, San Antonio, TX 78249, Texas, United States of America.

Also available: a schematic view as .pdf

Registration

Please follow the link on the registration page to register for the workshop.

• Dr. Dhireesha Kudithipudi, UT San Antonio
• Dr. Brad Aimone, Sandia National Laboratories
• Dr. Johannes Schemmel, Kirchhoff-Institute for Physics, Heidelberg University
• Dr. Suma George Cardwell, National Laboratories

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Authors: Jens Egholm Pedersen and Jörg Conradt.

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Authors: Bradley Theilman and James B. Aimone

Authros: Denis Kleyko, Christopher Kymn, Bruno A. Olshausen, Friedrich T. Sommer and E. Paxon Frady.

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We present major design issues for large-scale neuromorphic computing systems, and some of the trade-offs in designing hardware and software for such systems. Many of the detailed hardware trade-offs that have significant impact on overall energy efficiency depend strongly on the networks being mapped to the hardware. We describe ongoing work on creating a quantitative, full-stack approach to evaluating the trade-offs in neuromorphic system design, enabled by recently developed open-source tools for the design and implementation of asynchronous digital systems.

Authors: Claire Plunkett and Frances Chance.

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Authros: Adam McCaughan, Cory Merkel, Bakhrom Oripov, Andrew Dienstfrey, Sae Woo Nam and Sonia Buckley.

Authors: Anurag Daram and Dhireesha Kudithipudi.

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Authors: Karan Patel and Catherine Schuman.

Shuttle leaves at 18:00h from the meeting place and goes to "UTSA, San Pedro 1" (place of the welcome reception)

Address of the place: 506 Dolorosa St, San Antonio, TX 78204

(For people using their own car: parking space should likely be available at "Dolorosa Lot")

Shuttle leaves at 21:00h = 9:00 pm and returns to "UTSA main campus" (conference venue)

("Hardware" day)

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Authors: Ali Safa, Jonah Van Assche, Charlotte Frenkel, André Bourdoux, Francky Catthoor and Georges Gielen.

Authors: James Knight and Thomas Nowotny.

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Author: Davin Browner.

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Authors: Philipp Spilger, Elias Arnold, Luca Blessing, Christian Mauch, Christian Pehle, Eric Müller and Johannes Schemmel.

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Authors: Ali Safa, Ilja Ocket, Francky Catthoor and Georges Gielen.

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Authors: Wesley Brigner, Naimul Hassan, Xuan Hu, Christopher Bennett, Felipe Garcia-Sanchez, Can Cui, Alvaro Velasquez, Matthew Marinella, Jean Anne Incorvia and Joseph S. Friedman.

Authors: Frances Chance and Suma Cardwell.

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Location: iH-E-B Ballroom 1.104

("Applications day")

Authors: Sebastian Siegel, Tobias Ziegler, Younes Bouhadjar, Tom Tetzlaff, Rainer Waser, Regina Dittmann and Dirk Wouter.

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• Joe Hays (U.S Naval research Lab)
• Andrey Kanaev (NSF)
• Tina Kaarsberg (DOE)
• Jano Costard (SPRIN-D, Germany)
• Clare Thiem (AFRL)

Authors: Kenneth Stewart, Timothy Shea, Noah Pacik-Nelson, Eric Gallo and Andreea Danielescu.

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Authors: Diego Chavez Arana, Alpha Renner and Andrew Sornborger.

Authors: Charles Rizzo, Catherine Schuman and James Plank.

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Authors: George Brayshaw, Martin Pearson and Benjamin Ward-Cherrier.

Authors: Madeleine Abernot, Sylvain Gauthier, Théophile Gonos and Aida Todri-Sanial.

Authors: Jens Egholm Pedersen, Raghav Singhal and Jörg Conradt.

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Authors: Wallace Lawson, Anthony Harrison and Greg Trafton.

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Likely three slots in parallel

Confirmed tutorials:

• An Introduction to a Simulator for Super Conducting Optoelectronic Networks (Sim-SOENs)
• Sandia – Fugu Introductory Tutorial (offered twice with the same content)
• N2A -- An IDE for neural modeling
• Hands-on BrainScaleS - analog accelerated neuromorphic compute hardware (The hands-on session is offered twice with the same content). The BrainScaleS hardware systems are available for use online
• Intel Loihi 2: Build more impactful neuromorphic applications with Intel Loihi 2 and the open-source Lava framework

An Introduction to a Simulator for Super Conducting Optoelectronic Networks (Sim-SOENs)

This tutorial will suffice to impart a functional understanding of Sim-SOENs. Starting with the computational building blocks of SOEN neurons, we will cover the nuances and processing power of single dendrites, before building up to dendritic arbors within complex neuron structures. We will find it is straightforward to implement arbitrary neuron structures and even dendritic-based logic operations. Even at this single neuron level, we will already demonstrate efficacy on basic computational tasks. From there we will scale to network simulations of many-neuron systems, again with demonstrative use-cases. By the end of the tutorial, participants should be able to easily generate custom SOEN neuron structures and networks. These lessons will apply directly to researching in the computational paradigm that is to be instantiating on the burgeoning hardware of SOENs.
Format: Examples and instructions will be given in the form of Jupyter Notebook tutorials (already well into development). If it is conducive to the conference environment, these notebooks may be available for download and use in real-time. If this latter format is the case, practice exercises can be derived for active learning.

N2A -- An IDE for neural modeling

N2A is a tool for editing and simulating large-scale/complex neural models. These are written in a simple equation language with object-oriented features that support component creation and reuse. The tool compiles these models for various hardware targets ranging from neuromophic devices to supercomputers.
Format: The first hour will provide a general introduction to the integrated development environment (IDE) and cover basic use cases: model editing, running a simulation, sharing models via Git, and running parameter sweeps. The second hour will cover the basic LIF class hierarchy, techniques for designing your own component set, and integration with Sandia's Fugu tool.
Special Requirements: This will be a hands-on tutorial. N2A may be downloaded from https://github.com/frothga/n2a and run on your personal laptop.

BrainScaleS

A hands-on tutorial for online interactive use of the BrainScaleS neuromorphic compute system: from the first log-in via the EBRAINS Collaboratory to interactive emulation of small spiking neural networks. This hands-on tutorial is especially suitable for beginners (more advanced attendants are welcome as well). We are going to use the BrainScaleS tutorial notebooks for this event.
For using the BrainScaleS system during the tutorial (and also independently of the tutorial for own research, free of charge for evaluation) an EBRAINS account (also free of charge) is needed (get an EBRAINS account here).
More info on how to get started using BrainScaleS.
Format: Introductory presentation, followed by interactive hands-on tutorials. The attendants of the tutorial can a webbrowser on their own laptops to execute and change provided tutorials and explore on their own. Attendants will be able to continue accessing the systems with a generous test-quota also after the event
Preparation: Best to get an EBRAINS account here) ahead of time. We can also create a guest-account on the spot.

Fugu Introductory Tutorial

The tutorial will cover the basic design and practice of Fugu, a software package for composing spiking neural algorithms. We will begin will an introductory presentation on the motivation, design, and limitations of Fugu. Then, we will do two deep dive, interactive tutorials using jupyter notebooks. The first will cover how to use Fugu with pre-existing components, we call Bricks. The second will cover how to build a custom brick to perform a particular algorithm. In this case, the algorithm we choose will be an 80-20 network.
Format: Interactive
Preparation: Please clone and install: https://github.com/snl-nerl/fugu

Intel Loihi 2: Build more impactful neuromorphic applications with Intel Loihi 2 and the open-source Lava framework

Tim Shea from Intel Labs will demonstrate how you can program applications using the open-source Lava framework for neuromorphic computing and how to compile and run those applications on Intel Loihi 2 hardware. Lava is an excellent platform for neuromorphic researchers seeking more real-world impact because the high-level, modular API makes it easy for other labs to replicate your work while the flexible compiler architecture makes it easy to distribute your models across conventional and neuromorphic hardware. In this tutorial, you will learn how to build and run several example applications in Lava, including a deep learning model, a Dynamic Neural Field algorithm, a mathematical optimizer.

Format: This tutorial will introduce application programming in Lava through a series of Jupyter notebook tutorials. Attendees can follow along building the applications on their own laptops or using any free cloud-based notebook (e.g. Google Colab). Each application can be run locally on a standard CPU and the presenter will demonstrate how to run the examples on an Intel Kapoho Point neuromorphic system. All the necessary code and instructions are available at github.com/lava-nc.