NICE #8

(The NICE#8 was initially planned to take place in 2020, but had to be postponed to 2021 due to COVID-19.)

Note: the exact order and timing of the talks is so far draft!

Times in the agenda are in CET (Berlin), EDT (New York), PDT (Los Angeles) and UTC. (Some other time zones: Australia, Japan, China, India, ... or only CET ... )

Meeting venue:

• online as zoom video conference (live talks and Q and A). The zoom video conference client software (free of charge, available for Windows, Mac and Linux at zoom.us, also available for iOS and Android in the respective app stores ) is required.
• please register here for NICE 2021

NEUROTECH Forum (15 March 2021)

Info: just before NICE on 15 March 2021 the NEUROTECH Forum II will take place online (free of charge. Registration for the forum event). The topic is "Neuromorphic Computing Technologies: Opportunities, challenges and Applications Roadmap".

Welcome to NICE #8

Organizer Round

Keynote: Lessons from Loihi for the Future of Neuromorphic Computing

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Why is Neuromorphic Event-based Engineering the future of AI?

The BrainScaleS mobile platform

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Group photo (zoom screenshots)

Evaluating complexity and resilience trade-offs in emerging memory inference machines

Lightning talk: From clean room to machine room: towards accelerated cortical simulations on the BrainScaleS wafer-scale system

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Closed-loop experiments on the BrainScaleS-2 architecture

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Batch << 1: Why Neuromorphic Computing Architectures Suit Real-Time Workloads

Neuromorphic and AI research at BCAI (Bosch Center for Artificial Intelligence)

Mapping Deep Neural Networks on SpiNNaker2

Open mic / discussion

End of day I

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Tutorials: BrainScaleS and DYNAP-SE

Two tutorials/hands on in parallel:

• BrainScaleS (note: the same BrainScaleS hands on tutorial is also offered on Thursday, 10:00-13:00h CET)
  • about 30 min introduction
  • hands-on usage of the BrainScaleS system (via web browser). Limited number of participants.
• DYNAP-SE (note:the same DYNAP-SE hands on tutorial is also offered on Friday morning)
  • 1-hour live/interactive Dynapse demo: demo on a real Dynapse, take questions and implementing small changes from the audience.
  • 2-hour guided session where participants run a Jupyter notebook with simulations modelling Dynapse. This part is limited to 15 people per session.

For a description please see the tutorials page.

Tutorial: SpiNNaker hands-on

(Note: the same SpiNNaker hands on tutorial is also offered on Thursday, 21:00 - 22:30h CET)

For a description please see the tutorials page.

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NICE - day II

Keynote: From Brains to Silicon -- Applying lessons from neuroscience to machine learning

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A Neuromorphic Future for Classic Computing Tasks

Lightning talk: Benchmarking of Neuromorphic Hardware Systems

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Natural density cortical models as benchmarks for universal neuromorphic computers

Throughout evolution, the cortex has increased in volume from mouse to man by three orders of magnitude, while the architecture at the local scale of a cubic millimeter has largely been conserved in terms of the multi-layered structure and the density of synapses. Furthermore, local cortical networks are similar, independent of whether an area processes visual, auditory, or tactile information. This dual universality raises hope that fundamental principles of cortical computation can be discovered. Although a coherent view of these principles still remains missing, the universality motivated researchers already more than a decade go to start to develop neuromorphic computing systems based on the interaction between neurons by delayed point events and basic parameters of cortical architecture.

These systems need to be verified in the sense of accurately representing cortical dynamics and validated in the sense of simulating faster or more energy than software solutions on conventional computers. Such comparisons are only meaningful if they refer to implementations of the same neuronal network model. The role of models changes from mere demonstrations of functionality to quantitative benchmarks. In fields of computer science like computer vision and machine learning the definition of benchmarks helps to quantify progress and drives a constructive competition between research groups. The talk argues that neuromorphic computing needs to advance the development of benchmarks of increasing size and complexity.

A model of the cortical microcircuit [1] exemplifies the recent interplay and co-design of alternative hardware architectures enabled by a common benchmark. The model represents neurons with their natural number of synapses and at the same time captures the natural connection probability between neurons in the local volume. Consequently, all questions on the proper scaling of network parameters become irrelevant. The model constitutes a milestone for neuromorphic hardware systems as larger cortical models are necessarily less densely connected.

As metrics we discuss the energy consumed per synaptic event and the real-time factor. We illustrate the progress in the past few years and show that a single conventional compute node still keeps up with neuromorphic hardware and achieves sub real-time performance. Finally, the talk exposes the limitations of the microcircuit model as a benchmark and positions cortical multi-area models [2] as a biologically meaningful way of upscaling benchmarks to the next problem size.

• [1] Potjans & Diesmann (2014), Cerebral Cortex 24:785–806
• [2] Schmidt, Bakker, Shen, Bezgin, Diesmann, van Albada (2018) PLOS Comput Biol 14(10):e1006359

This work is partially supported by the European Union's Horizon 2020 (H2020) funding framework under grant agreement no. 945539 (Human Brain Project SGA3) and the Helmholtz Association Initiative and Networking Fund under project number SO-092 (Advanced Computing Architectures, ACA).

Poster Lightning Talks

1 min - 1 slide poster appetizers

Poster session A and coffee

Platform-Agnostic Neural Algorithm Composition using Fugu

Lightning talk: Implementing Backpropagation for Learning on Neuromorphic Spiking Hardware

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Inductive bias transfer between brains and machines

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Lightning talk: Spike Latency Reduction generates Efficient Predictive Coding

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Lightning talk: Cognitive Domain Ontologies: HPCs to Ultra Low Power Neuromorphic Platforms

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Open mic / discussion

(break)

Tutorial: BrainScaleS hands-on

(Note: the same BrainScaleS hands on tutorial is also offered on Tuesday evening)

• about 30 min introduction
• hands-on usage of the BrainScaleS system (via web browser). Limited number of participants.

For a description of the pre-requirements, please see the tutorials page.

(break)

NICE - day III

Keynote: Biological inspiration for improving computing and learning in spiking neural networks

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On the computational power and complexity of Spiking Neural Networks

Evolutionary Optimization for Neuromorphic Systems

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An event-based gas sensing device that resolves fast transients in a turbulent environment

Sequence learning, prediction, and generation in networks of spiking neurons

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Poster session b and coffee

Conductance-based dendrites perform reliability-weighted opinion pooling

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Lightning talk: Natural gradient learning for spiking neurons

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Making spiking neurons more succinct with multi-compartment models

Lightning talk: The Computational Capacity of Mem-LRC Reservoirs

Open mic / discussion

(break)

Tutorial: SpiNNaker hands-on

(Note: the same SpiNNaker hands on tutorial is also offered on Wednesday, 10:30-12:00h CET)

For a description please see the tutorials page.

Tutorial:: DYNAP-SE

(Note: the same DYNAP-SE tutorial is also offered on Tuesday evening)

• 1-hour live/interactive Dynapse demo: demo on a real Dynapse, take questions and implementing small changes from the audience.
• 2-hour guided session where participants run a Jupyter notebook with simulations modelling Dynapse. This part is limited to 15 people per session.

(break)

NICE - day IV

Keynote: Bottom-up and top-down neuromorphic processor design: Unveiling roads to embedded cognition

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Lightning talk: Subspace Locally Competitive Algorithms

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Programming neuromorphic computers: PyNN and beyond

Lightning talk: TBD

BrainScaleS: Development Methodologies and Operating System

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Lightning talk: Evolving Spiking Neural Networks for Robot Sensory-motor Decision Tasks of Varying Difficulty

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Coffee break

Relational Neurogenesis for Lifelong Learning Agents

Lightning talk: Fast and deep neuromorphic learning with first-spike coding

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Lightning talk: Neuromorphic Computing for Spacecraft’s Terrain Relative Navigation: A Case of Event-Based Crater Classification Task

Beyond Backprop: Different Approaches to Credit Assignment in Neural Nets

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Lightning talk: Comparing Neural Accelerators & Neuromorphic Architectures The False Idol of Operations

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Real-time Mapping on a Neuromorphic Processor

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Wrap up / farewell

End of NICE 2021