Tuesday, 23 April 2024 | |||
08:00 | NICE 2024-- day 1NICE 2024RegistrationFor registration, please see the registration options and link to the registration form. Venue
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08:00 | NICE 2024 agendaTimes listed are Pacific Daylight Time (PDT). It is also possible to show the agenda with other time zones listed, which are relevant for: Africa, America, Australia, China, Europe, India, Japan | ||
08:00‑08:30 (30 min) | Registration and breakfast | ||
08:30‑08:45 (15+5 min) | Welcome and opening | Gert Cauwenberghs, Duygu Kuzum, Tajana Rosing (Institute for Neural Computation and Jacobs School of Engineering, UC San Diego) Miroslav Krstić (Associate Vice Chancellor for Research, UC San Diego) | |
08:50‑09:35 (45+5 min) | Organisers round | Members of the organising committees | |
09:40‑10:25 (45+5 min) | Keynote: Brains and AI show presentation.pdf (public accessible) show talk video | Terry Sejnowski (Salk Institute for Biological Studies) | |
10:30‑11:00 (30 min) | Break | ||
11:00‑11:25 (25+5 min) | Biological Dynamics Enabling Training of Binary Recurrent Networks show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10549632 | William Chapman (Sandia National Laboratories) | |
11:30‑11:40 (10+5 min) | Towards Convergence Intelligence – neuromorphic engineering and engineered organoids for neurotechnology show presentation.pdf (public accessible) show talk video | Dr. Grace Hwang (Program Director, National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NINDS)/ U.S. BRAIN Initiative) | |
11:45‑12:10 (25+5 min) | Invited talk: Learning algorithms for spiking and physical neural networks show presentation.pdf (public accessible) show talk video | Friedemann Zenke (Friedrich Miescher Institute for Biomedical Research & University of Basel) | |
12:15‑12:30 (15 min) | Poster teasers 1-min "this is my poster content" teasers
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12:30‑14:00 (90 min) | Poster-lunch (posters + finger food)
Posters
Talk-Posters
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14:00‑14:25 (25+5 min) | SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks show talk video Publication DOI: 10.1109/NICE61972.2024.10549198 | Sreyes Venkatesh (UC Santa Cruz) | |
14:30‑14:40 (10+5 min) | Expressive Dendrites in Spiking Networks show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10548485 | Mark Plagge (Sandia National Laboratories) | |
14:45‑15:10 (25+5 min) | Text-to-Events: Synthetic Event Camera Streams from Conditional Text Input show presentation.pdf (public accessible) video (restricted access) Publication DOI: 10.1109/NICE61972.2024.10549580 | Shih-Chii Liu (Institute of Neuroinformatics, University of Zurich and ETH Zurich) | |
15:15‑15:45 (30 min) | Break | ||
15:45‑16:10 (25+5 min) | Embracing the Hairball: An Investigation of Recurrence in Spiking Neural Networks for Control show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10548512 | Katie Schuman (University of Tennessee) | |
16:15‑17:00 (45 min) | Open mic / discussion -- day I speakers | ||
17:00‑17:30 (30 min) | Misha Mahowald Prizes show talk video
| Tobi Delbruck (Inst. for Neuroinformatics, UZH-ETH Zurich) | |
17:30‑18:00 (30 min) | Misha Mahowald Prizes show talk video
| Carver Mead (California Institute of Technology) | |
18:00‑19:00 (60 min) | Reception and celebration of 35+ years of neuromorphic engineering |
Wednesday, 24 April 2024 | |||
08:00 | NICE 2024 - Day II | ||
08:00‑08:30 (30 min) | Breakfast | ||
08:30‑09:15 (45+5 min) | Keynote: Hearing with Silicon Cochleas | Shih-Chii Liu (Institute of Neuroinformatics, University of Zurich and ETH Zurich) | |
09:20‑09:30 (10+5 min) | Explaining Neural Spike Activity for Simulated Bio-plausible Network through Deep Sequence Learning show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10549689 | Shruti R Kulkarni (Oak Ridge National Laboratory) | |
09:35‑10:00 (25+5 min) | Invited talk: Hardware Accelerators for Brain-Inspired Computing show talk video | John Arthur (IBM Research) | |
10:05‑10:30 (25+5 min) | Hardware-aware Few-shot Learning on a Memristor-based Small-world Architecture show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10548824 | Karthik Charan Raghunathan (Institute of Neuroinformatics, ETH and UZH Zurich) | |
10:35‑11:05 (30 min) | Break | ||
11:05‑11:30 (25+5 min) | Spiking Physics-Informed Neural Networks on Loihi-2 Publication DOI: 10.1109/NICE61972.2024.10548180 | Brad Theilman (Sandia National Laboratories) | |
11:35‑11:45 (10+5 min) | jaxsnn: Event-driven Gradient Estimation for Analog Neuromorphic Hardware show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10548709 | Eric Müller (BrainScaleS, Heidelberg University) | |
11:50‑12:00 (10+5 min) | Late-breaking-news: Distributed Neural State Machines on Loihi 2 show presentation.pdf (public accessible) | Alpha Renner (Forschungszentrum Jülich, Germany) | |
12:05‑12:20 (15 min) | Poster teasers 1-min "this is my poster content" teasers
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12:20‑12:30 (10 min) | Group photo | ||
12:30‑14:00 (90 min) | Poster-lunch (posters + finger food)
Posters
Talk-Posters
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14:00‑14:10 (10+5 min) | Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10548306 | Sai Sukruth Bezugam (Department of Electrical and Computer Engineering, University of California Santa Barbara) | |
14:15‑14:40 (25+5 min) | Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10549719 | Chris Kymn (UC Berkeley), Sonia Mazelet (UC Berkeley) | |
14:45‑14:55 (10+5 min) | Towards Chip-in-the-loop Spiking Neural Network Training via Metropolis-Hastings Sampling show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10548355 | Ali Safa (imec and KU Leuven) | |
15:00‑15:10 (10+5 min) | Leveraging Sparsity of SRNNs for Reconfigurable and Resource-Efficient Network-on-Chip show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10548940 | Manu Rathore (TENNLab - Neuromorphic Architectures, Learning, Applications (The University of Tennessee, Knoxville)) | |
15:15‑15:45 (30 min) | Break | ||
15:45‑16:10 (25+5 min) | Invited talk: Towards fractional order dynamics neuromorphic elements show presentation.pdf (public accessible) show talk video | Fidel Santamaria (Department of Neuroscience, Developmental and Regenerative Biology, University of Texas at San Antonio) | |
16:15‑16:25 (10+5 min) | Compute-in-Memory with 6T-RRAM Memristive Circuit for Next-Gen Neuromorphic Hardware Publication DOI: 10.1109/NICE61972.2024.10548860 | Kang Jun Bai (Air Force Research Laboratory) | |
16:30‑17:30 (60 min) | Open mic / discussion - day II speakers | ||
17:30 | End of the second day |
Thursday, 25 April 2024 | |||
08:00 | Day III | ||
08:00‑08:30 (30 min) | Breakfast | ||
08:30‑09:15 (45+5 min) | Keynote: NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems show presentation.pdf (public accessible) show talk video | Jason Yik (School of Engineering and Applied Sciences, Harvard) | |
09:20 | IEEE EMBS Forum on Intelligent Healthcare | ||
09:20‑09:25 (5 min) | Introduction to the IEEE EMBS Forum on Intelligent Healthcare show talk video | Gert Cauwenberghs (UC San Diego) | |
09:25‑09:35 (10 min) | AI for healthcare show talk video | Margot Wagner (Salk Institute) | |
09:35‑09:45 (10 min) | Boxes are pre-digital -- Discovering physiological diversity with AI show talk video | Benjamin Smarr (UC San Diego) | |
09:45‑09:55 (10 min) | Organoid intelligence show talk video | Francesca Puppo (UC San Diego) | |
09:55‑10:05 (10 min) | Health & AI: Flexible Engineered Learning Systems show talk video | Dr. Grace Hwang (NIH / NINDS) | |
10:05‑10:15 (10 min) | EMBS Strategic Plan: from Vision to Implementation show talk video | Metin Akay (IEEE EMBS) | |
10:20‑10:50 (30 min) | Break | ||
10:50‑11:00 (10+5 min) | One-Shot Auditory Blind Source Separation via Local Learning in a Neuromorphic Network show talk video | Patrick Abbs (Cambrya, LLC) | |
11:05‑11:30 (25+5 min) | Invited talk: TENN: A highly efficient transformer replacement for edge and event processing. show presentation.pdf (public accessible) | M Anthony Lewis (BrainChip) | |
11:35‑11:45 (10+5 min) | A Recurrent Dynamic Model for Efficient Bayesian Optimization show talk video Publication DOI: 10.1109/NICE61972.2024.10548051 | P. Michael Furlong (Centre for Theoretical Neuroscience/Systems Design Engineering, University of Waterloo) | |
11:50‑12:15 (25+5 min) | Invited talk: Strategic & Large-Scale Considerations of Neuromorphic Computing show presentation.pdf (public accessible) show talk video | Craig Vineyard (Sandia National Laboratory) | |
12:20‑12:30 (10+5 min) | GPU-RANC: A CUDA Accelerated Simulation Framework for Neuromorphic Architectures show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10548776 | Joshua Mack (Department of Electrical & Computer Engineering, University of Arizona) | |
12:35‑13:35 (60 min) | Lunch | ||
13:35‑13:45 (10+5 min) | Late-breaking-news: Neuromodulated mixture of experts: A prefrontal cortex inspired architecture for lifelong learning show presentation.pdf (public accessible) show talk video | Clara N Yi (Salk Institute for Biological Studies) | |
13:50‑14:15 (25+5 min) | Energy Efficient Implementation of MVM Operations Using Filament-free Bulk RRAM Arrays show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10549369 | Dr. Ashwani Kumar (Electrical and Computer Engineering, UC San Diego) | |
14:20‑14:30 (10+5 min) | TickTockTokens: a minimal building block for event-driven systems show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10549408 | Johannes Leugering (Institute for Neural Computation, Bioengineering Dept., UC San Diego) | |
14:35‑14:45 (10+5 min) | Late-breaking-news: Brain-Inspired Hypervector Processing at the Edge of Large Language Models show presentation.pdf (public accessible) show talk video | Goktug Ayar (University of Louisiana at Lafayette) | |
14:50‑15:00 (10+5 min) | Spiking Neural Network-based Flight Controller show presentation.pdf (public accessible) show talk video Publication DOI: 10.1109/NICE61972.2024.10548609 | Diego Chavez Arana (New Mexico State University) | |
15:05‑15:35 (30 min) | Coffee break | ||
15:35‑16:00 (25+5 min) | PETNet– Coincident Particle Event Detection using Spiking Neural Networks show talk video Publication DOI: 10.1109/NICE61972.2024.10549584 | Jan Debus (ETH Zurich) | |
16:05‑16:15 (10+5 min) | NeRTCAM: CAM-Based CMOS Implementation of Reference Frames for Neuromorphic Processors show talk video Publication DOI: 10.1109/NICE61972.2024.10548603 | Harideep Nair (Carnegie Mellon University.) | |
16:20‑17:20 (60 min) | Open mic / discussion - day III speakers | ||
17:20‑17:30 (10 min) | Final words -- invitation to NICE 2025 | ||
17:30 | End of day 3 and of the talk-days of NICE 2024 |
Friday, 26 April 2024 | |||||||||
08:30 | Tutorial dayVenueThe tutorial day takes place at the San Diego Supercomputer Center SDSC (SDSC visitor information page) There is no reserved parking available for the Friday tutorial.
Tutorials are in three different rooms, all located at San Diego Supercomputer Center, East Expansion; Level B1
TutorialsThese 9 tutorial suggestions have been selected.
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08:30‑10:30 (120 min) | Tutorial slot I Three tutorials in parallel Simulation Tool for Asynchronous Cortical Streams (STACS)In this tutorial, we will explore how to define networks and take advantage of the parallel capabilities of the spiking neural network (SNN) simulator STACS (Simulation Tool for Asynchronous Cortical Streams) https://github.com/sandialabs/STACS. We will primarily be focused on the stand-alone simulator functionality of STACS, which is written in C++, and may be loosely interfaced through Python. Here, a common use case of defining a neural network from preconstructed neuron and synapse models will be covered, with an optional hands-on exercise (through a text editor and the command line directly, and from a Jupyter Notebook). STACS was developed in part to provide a scalable simulation backend that may support large-scale SNN experiments. Developed to be parallel from the ground up, STACS leverages the highly portable Charm++ parallel programming framework https://charm.cs.illinois.edu, which expresses a paradigm of asynchronous message-driven parallel objects. Here, STACS takes advantage of the multicast communication pattern supported by Charm++ to match the irregular communication workload of biological scale models. In addition to the parallel runtime, STACS also implements a memory-efficient distributed network data structure for network construction, simulation, and serialization. This allows for both large-scale and long running simulations (e.g. through checkpoint/restart) on high performance computing (HPC) systems. For network serialization, STACS uses an SNN extension to the popular distributed compressed sparse row (dCSR) format used in graph partitioners, SNN-dCSR. This supports partitioning a network model along its connectivity structure or spatial layout to facilitate more efficient communication by reducing the volume of spikes that are exchanged across compute resources. It also serves as a portable intermediate representation for interoperability between tools within the neural computing ecosystem and for different neuromorphic backends. For preparations: if folks could install Charm++ and STACS (from their respective repositories, https://github.com/UIUC-PPL/charm, https://github.com/sandialabs/STACS) on their computers (Supported on Linux, MacOS) beforehand, that could be good, but the speaker plans on going through those installation steps briefly at the tutorial too since it can be a bit involved. Hands-on tutorial: BrainScaleS neuromorphic compute systemA 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). Information about the EBRAINS NMC systems (SpiNNaker and BrainScaleS) is available at https://ebrains.eu/nmc 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: https://ebrains.eu/register The attendants of the tutorial will use 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. An Integrated Toolbox for Creating Neuromorphic Edge ApplicationsSpiking Neural Networks (SNNs) and neuromorphic models are more efficient and have more biologically realism than the activation functions typically used in deep neural networks, transformer models and generative AI. SNNs have local learning rules, can learn on small data sets, and adaptive by neuromod- ulation. However, although the research has discovered their advantages, there are still few compelling practical applications, especially at the edge where sensors and actuators need to be processed in a timely fashion. One reason for this might be that SNNs are much more challenging to understand, build, and operate due to their intrinsic properties. For instance, the mathematical foundation involves differential equations rather than basic activation functions of the neurons. To address these challenges, we have developed CARLsim++. CARLsim++ can lead to rapid development of neuromorphic applications for simulation or edge processing.It is an integrated toolbox that enables fast and easy creation of neuromorphic applications. It encapsulates the mathematical intrinsics and low-level C++ programming by providing a graphical user interface for users who do not have a background in software engineering but still want to create neuromorphic models. In this tutorial, we will demonstrate how one can easily configure inputs and outputs to a physical robot using CARLsim++.
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10:30‑11:00 (30 min) | Coffee break | ||||||||
11:00‑13:00 (120 min) | Tutorial slot II Three tutorials in parallel
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13:00‑14:00 (60 min) | Lunch
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14:00‑16:00 (120 min) | Tutorial slot III Three tutorials in parallel | ||||||||
16:00 | End of NICE 2024 |