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Florian Kelber

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Articles by Florian Kelber

Rapid and energy-efficient ultra-large library screening for drug discovery on a SpiNNaker2 neuromorphic chip

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By Florian Kelber, Bernhard Vogginger, Paul Eisenhuth, Max Beining, Johannes Partzsch, Christian Mayr, Jens Meiler, Vivian Ehrlich
| Nature Verified
Abstract The virtual screening of make-on-demand small molecule libraries can prioritize drug candidates for rapid experimental validation to accelerate pre-clinical drug discovery. As ultra-large libraries grow to billions of compounds, exhaustive screening incurs prohibitive costs and energy consumption. We address this challenge with the neuromorphic SpiNNaker2 system, designed for massively parallel AI tasks.
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SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning

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By Hector Gonzalez, Héctor González Verified, Jiaxin Huang, Florian Kelber, Khaleelulla Khan Nazeer
| tu-dresden.de
Contributors Hector A.
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OctopuScheduler: On-Chip DNN Scheduling on the SpiNNaker2 Neuromorphic MPSoC

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By Tim Langer, Matthias Jobst, Chen Liu, Florian Kelber
| tu-dresden.de
We present OctopuScheduler, the first generalized on-chip scheduling framework for the accelerated inference of non-spiking deep neural networks (DNNs) on the neuromorphic hardware platform SpiNNaker2. The goal of OctopuScheduler is to flexibly support a wide variety of state-of-the-art DNN architectures for different domains, moving from an application-specific custom implementation to a generally applicable framework, simplifying the access to the SpiNNaker2 platform.
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Fast Switching Serial and Parallel Paradigms of SNN Inference on Multi-Core Heterogeneous Neuromorphic Platform SpiNNaker2

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By Jiaxin Huang, Bernhard Vogginger, Florian Kelber, Hector Gonzalez, Héctor González
| tu-dresden.de
Huang, J., Vogginger, B. , Kelber, F., Gonzalez, H., Knobloch, K. & Mayr, C. G., 2024 . Research output: Preprint/Documentation/Report › Preprint
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CA-CFAR is Convolution: Fast Target Detection with Machine Learning Accelerator

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By Chen Liu, TUD Dresden, Florian Kelber, Bernhard Vogginger
| tu-dresden.de
In radar target detection, Constant False Alarm Rate is a commonly employed detector known for its simplicity and effectiveness. Its sliding-window detection mechanism possesses computational similarity to convolutional operations in machine learning. With the increasing emergence of AI-enhanced radar processing algorithms, systems at the edge tend to be equipped with machine learning accelerators to expedite matrix multiplications and convolutions.
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A 16-Channel Fully Configurable Neural SoC With 1.52 μW/Ch Signal Acquisition, 2.79 μW/Ch Real-Time Spike Classifier, and 1.79 TOPS/W Deep Neural Network Accelerator in 22 nm FDSOI

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By Franz Marcus Schuffny, Richard George, Florian Kelber, Heiner Bauer
| tu-dresden.de
Abstract External IDs PubMed 35025750 Keywords accelerator architectures, Biomedical electronics, biomedical signal processing, digital integrated circuits, energy efficiency, implantable devices, neural recording system, spike sorting, unsupervised learning
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