|Monday, 16 March 2020|
|NICE 2020 in Heidelberg|
6th March 2020: We are sorry to announce that NICE 2020, scheduled to be held on March 17-20 2020, will be postponed to a later date. Please see here for the new date in March 2021
Meeting venue: Kirchhoff-Institute for Physics, Im Neuenheimer Feld 227, D-69117 Heidelberg, Germany
Pre-NICE events on
NICE 2020 Welcome reception
at the meeting venue.
The reception is also open for the participants of the NEUROTECH event Future Application Directions for Neuromorphic Computing Technologies
|Tuesday, 17 March 2020|
|08:45||NICE 2020, workshop day I -- NOTE: NICE will be POSTPONED!|
(Registration booth opens at 8:30h)
|Welcome to NICE 2020|
|Keynote I||Mike Davies (Intel)|
|Luping Shi (Tsinghua University)|
|Evaluating complexity and resilience trade-offs in emerging memory inference machines|
|Johannes Schemmel (Heidelberg University)|
|Lightning talk: From clean room to machine room: towards accelerated cortical simulations on the BrainScaleS wafer-scale system||Sebastian Schmitt (Heidelberg University)|
|Poster Lightning Talks|
1 min - 1 slide poster appetizers
|all Poster Presenters|
|Lunch, poster setup, demonstrators setup|
|Group photo at NICE|
(The group photo will be placed on the internet. By showing up for the photo you grant your permission for the publication of the photo)
|Why is Neuromorphic Event-based Engineering the future of AI?|
|Neuromorphic and AI research at BCAI (Bosch Center for Artificial Intelligence)||Thomas Pfeil|
|Mapping Deep Neural Networks on SpiNNaker2||Florian Kelber et al.|
|Closed-loop experiments on the BrainScaleS-2 architecture||Korbinian Schreiber et al.|
|Lightning talk: Adaptive control for hindlimb locomotion in a simulated mouse through temporal cerebellar learning||Thomas Passer Jensen (Technical University of Denmark)|
|Open mic / discussions|
The max. poster size is A0, orientation PORTRAIT (841 mm wide x 1189 mm high)
|Wednesday, 18 March 2020|
|08:45||NICE 2020, workshpo day II -- NOTE: NICE will be postponed!|
|Welcome / overview|
|On the computational power and complexity of Spiking Neural Networks||(Nils Donselaar)|
|The speed of sequence processing in biological neuronal networks||Younes Bouhadjar et. al|
|Conductance-based dendrites perform reliability-weighted opinion pooling||Jakob Jordan et al.|
|Lightning talk: Natural gradient learning for spiking neurons||Elena Kreutzer et al.|
|Lightning talk: The Computational Capacity of Mem-LRC Reservoirs||Forrest Sheldon et al.|
|Making spiking neurons more succinct with multi-compartment models||Johannes Leugering|
|Evolutionary Optimization for Neuromorphic Systems||Catherine Schuman et al.|
|Lightning talk: Implementing Backpropagation for Learning on Neuromorphic Spiking Hardware||Andrew Sornborger et al.|
|Lightning talk: Spike Latency Reduction generates Efficient Predictive Coding||Pau Vilimelis Aceituno et al.|
|Special coffee break: EINC & BrainScaleS 1|
|Real-time Mapping on a Neuromorphic Processor||(Konstantinos Michmizos |
|Inductive bias transfer between brains and machines||Fabian Sinz|
|Open mic / discussion|
|Thursday, 19 March 2020|
|08:45||NICE 2020, workshop day III -- NOTE: NICE will be postponed!|
|Welcome / overview|
|Keynote: Bottom-up and top-down neuromorphic processor design: Unveiling roads to embedded cognition||Charlotte Frenkel|
|A Neuromorphic Future for Classic Computing Tasks||Brad Aimone|
|Lightning talk: Benchmarking of Neuromorphic Hardware Systems||Christoph Ostrau et al.|
|Lightning talk: Evolving Spiking Neural Networks for Robot Sensory-motor Decision Tasks of Varying Difficulty||(J David Schaffer)|
|Natural density cortical models as benchmarks for universal neuromorphic computers||Markus Diesmann|
|Platform-Agnostic Neural Algorithm Composition using Fugu||William Severa|
|Programming neuromorphic computers: PyNN and beyond||Andrew Davison|
|Lightning talk: Caspian: A Neuromorphic Development Platform||(John (Parker) Mitchell)|
|BrainScaleS: Development Methodologies and Operating System||Eric Müller|
|Lightning talk: Cognitive Domain Ontologies: HPCs to Ultra Low Power Neuromorphic Platforms|
Tarek Taha, Chris Yakopcic, Nayim Rahman, Tanvir Atahary and Scott Douglass
The Cognitively Enhanced Complex Event Processing (CECEP) agent-based decision-making architecture is being developed at AFRL/RHCI . Within this agent, the Cognitive Domain Ontology (CDO) component is the slowest for most applications of the agent. We show that even after acceleration on a high performance server based computing system enhanced with a high end graphics processing unit (GPU), the CDO component does not scale well for real time use on large problem sizes. Thus, to enable real time use of the agent, particularly in power constrained environments (such as autonomous air vehicles), alternative implementations of the agent logic are needed. These alternative implementations need to utilize different algorithms that implement the CDO logic and need to be targeted to much lower power (and weight) computing systems than GPU enabled servers (which can consume over 500W and weigh over 50lbs).
The objective of this work was to carry out an initial design space search of algorithms and hardware for decision making through the domain knowledge component of CECEP (the CDO) [2-5]. Several algorithmic and circuit approaches are proposed that span across six hardware options of varying power consumption and weight (ranging from over 1000W to less than 1W). The algorithms range from exact solution producers optimized for running on a cluster of high performance computing systems  to approximate solution producers running fast on low power neuromorphic hardware [6-9]. The loss in accuracy for the approximate approaches is minimal, making them well suited to SWaP constrained systems, such as UAVs. The exact solution approach on an HPC will give confidence that the best answer has been evaluated (although this may take some time to generate).
 T. Atahary, T. Taha, F. Webber, and S. Douglass, “Knowledge mining for cognitive agents through path based forward checking,” 16th IEEE/ACIS SNPD, pp. 1-8, June, 2015.
 C. Yakopcic, N. Rahman, T. Atahary, T. M. Taha, and S. Douglass, “Cognitive Domain Ontologies in a Memristor Crossbar Architecture,” IEEE National Aerospace and Electronics Conference (NAECON), pp. 76-83, Dayton, OH, June 2017.
 N. Rahman, T. Atahary, T. Taha, S. Douglass, "A pattern matching approach to map cognitive domain ontologies to the IBM TrueNorth Neurosynaptic System." 2017 Cognitive Communications for Aerospace Applications Workshop (CCAA). IEEE, 2017.
 N. Rahman, C. Yakopcic, T. Atahary, R. Hasan, T. M. Taha, and S. Douglass, “Cognitive Domain Ontologies in Lookup Tables Stored in a Memristor String Matching Architecture,” 30th annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-4, Windsor, Ontario, April 2017.
 N. Rahman, T. Atahary, C. Yakopcic, T. M. Taha, Scott Douglass, “Task Allocation Performance Comparison for Low Power Devices,” IEEE National Aerospace and Electronics Conference (NAECON), pp. 247-253, Dayton, OH, July, 2018.
 C. Yakopcic, T. Atahary, T. M. Taha, A. Beigh, and S. Douglass, “High Speed Approximate Cognitive Domain Ontologies for Asset Allocation based on Isolated Spiking Neurons,” IEEE National Aerospace and Electronics Conference (NAECON), pp. 241-246, Dayton, OH, July, 2018.
 C. Yakopcic, N. Rahman, T. Atahary, T. M. Taha, A. Beigh, and S. Douglass, “High Speed Approximate Cognitive Domain Ontologies for Constrained Asset Allocation based on Spiking Neurons,” IEEE National Aerospace and Electronics Conference (NAECON), 2019.
 C. Yakopcic, T. Atahary, N. Rahman, T. M. Taha, A. Beigh, and S. Douglass, “High Speed Approximate Cognitive Domain Ontologies for Asset Allocation Using Loihi Spiking Neurons,” IEEE/INNS International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July, 2019.
 C. Yakopcic, J. Freeman, T. M. Taha, S. Douglass, and Q. Wu, “Cognitive Domain Ontologies Based on Loihi Spiking Neurons Implemented Using a Confabulation Inspired Network,” IEEE Cognitive Communications for Aerospace Applications Workshop, June, 2019.
|Tarek Taha et al.|
|Lightning talk: Comparing Neural Accelerators & Neuromorphic Architectures The False Idol of Operations||Craig Vineyard et al.|
|Lightning talk: Subspace Locally Competitive Algorithms||Dylan Paiton et al.|
|Lightning talk: Fast and deep neuromorphic learning with first-spike coding||Julian Göltz et al.|
|Lightning talk: Neuromorphic Graph Algorithms: Extracting Longest ShortestPaths and Minimum Spanning Trees|
|Lightning talk: Neuromorphic Computing for Spacecraft’s Terrain Relative Navigation: A Case of Event-Based Crater Classification Task||Kazuki Kariya et al.|
|Beyond Backprop: Different Approaches to Credit Assignment in Neural Nets|
|Batch << 1: Why Neuromorphic Computing Architectures Suit Real-Time Workloads ||Jonathan Tapson (GrAI Matter Labs)|
|Relational Neurogenesis for Lifelong Learning Agents||Tej Pandit et al.|
|open mic / discussions|
|Wrap-up / adjourn|
|18:30||End of NICE 2020 for non-tutorial attendants|
|Dinner (only for tutorial attendants)|
|Friday, 20 March 2020|
|09:00||NICE 2020, Tutorials day: NOTE: NICE will be POSTPONED!|
The tutorial day can be booked as one of the registration options. On the tutorial day hands-on interactive tutorials with several different neuromorphic compute systems will be offered:
|Tutorial (and coffee)|
In parallel ("choose one"):
|Lunch (only for tutorial participants)|
|Tutorial (and coffee)|
In parallel ("choose one"):
|15:00||End of the NICE 2020 tutorial day|