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DTSTART;TZID=Europe/Zurich:20240205T180000
DTEND;TZID=Europe/Zurich:20240205T200000
DTSTAMP:20260830T163428
CREATED:20240105T080554Z
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SUMMARY:Jens E. Pedersen @ ONM - NIR: A Unified Instruction Set for Brain-Inspired Computing
DESCRIPTION:Have you wondered how to use neuromorphic hardware platforms? \nAre you depressed by your power bill after you bought your >400W GPU rig? \nThen you came to the right place! \nIn this workshop\, we will show you how to move models from your favourite framework directly to neuromorphic hardware with 1-2 lines of code! We will present the technology behind\, the Neuromorphic Intermediate Representation \, and demonstrate how we can use it to run a live spiking convnet on the Speck chip. \nNIR is currently supported by Intel Loihi \, Speck \, SpiNNaker2 \, Xylo and a host of simulators\, including Norse \, snnTorch \, and Spyx . \nJoin us on the 5th of February to get your own hands-on experience with NIR and neuromorphic hardware! \nAll it requires is a computer and a bit of Python knowledge. \nAgenda: \n\n18:00 – 19:00: NIR introduction\n\nMotivation: coupling neuromorphic hardware and software\nDemonstrating NIR: from PyTorch to Speck\nQ&A\n\n\n19:00 – 20:00: Workshop\n\nHands-on experience with NIR via Jupyter Notebooks or custom models\nQ&A and collaborative discussions\n\n\n\nSpeakers: \n\nJens E. Pedersen \, PhD at the Neurocomputing Systems lab at KTH Royal Institute of Technology\, Sweden\n\nNote: The event will be hosted virtually. Stay tuned for the video link and further updates. \n\n\n\n\n\nAbout the Speaker\nJens is a computer scientist studying his PhD in neuromorphic computing at the KTH Royal Institute of Technology. Jens co-authored the Norse simulator and the AEStream event-based streaming library.
URL:https://www.neuropac.info/event/jens-e-pedersen-onm-nir-a-unified-instruction-set-for-brain-inspired-computing/
LOCATION:Online
CATEGORIES:Talk,Workshop
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DTSTART;TZID=Europe/Zurich:20240227T180000
DTEND;TZID=Europe/Zurich:20240227T193000
DTSTAMP:20260830T163428
CREATED:20240105T080743Z
LAST-MODIFIED:20240308T111522Z
UID:10000275-1709056800-1709062200@www.neuropac.info
SUMMARY:Aaron Spieler @ ONM - The ELM Neuron: An Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks
DESCRIPTION:From the Open Neuromorphic website. \nBiological cortical neurons are remarkably sophisticated computational devices\, temporally integrating their vast synaptic input over an intricate dendritic tree\, subject to complex\, nonlinearly interacting internal biological processes. \nWith the aim to explore the computational implications of leaky memory units and nonlinear dendritic processing\, we introduce the Expressive Leaky Memory (ELM) neuron model\, a biologically inspired phenomenological model of a cortical neuron. Remarkably\, by exploiting a few such slowly decaying memory-like hidden states and two-layered nonlinear integration of synaptic input\, our ELM neuron can accurately match the aforementioned input-output relationship with under ten-thousand trainable parameters. \nWe evaluate the model on various tasks with demanding temporal structures\, including the Long Range Arena (LRA) datasets\, as well as a novel neuromorphic dataset based on the Spiking Heidelberg Digits dataset (SHD-Adding). The ELM neuron reliably outperforms the classic Transformer or Chrono-LSTM architectures on these tasks\, even solving the Pathfinder-X task with over 70% accuracy (16k context length). \n\n\n\n\n\nAbout the Speaker\nAaron Spieler is a computational neuroscientist passionate about exploring the intersection of deep learning and neuroscience. After earning his Bachelor’s in Computer Science from the University of Potsdam\, he undertook an extended internship at Amazon Web Services working in deep learning based forecasting\, before further specializing with a Master’s in Computational Neuroscience at the University of Tübingen. Throughout his Master’s thesis and a subsequent internship at the Max Planck Institute for Intelligent Systems\, Aaron focused on phenomenological neuron modeling with applications to long-range prediction tasks. Pursuing this work allowed him to collaborate with excellent researchers from diverse backgrounds\, including Prof. Bernhard Schölkopf and Prof. Anna Levina.
URL:https://www.neuropac.info/event/aaron-spieler-onm-the-elm-neuron-an-efficient-and-expressive-cortical-neuron-model-can-solve-long-horizon-tasks/
LOCATION:Online
CATEGORIES:Talk,Workshop
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