%0 Generic
%A Breitwieser, Oliver Julien
%C Heidelberg
%D 2021
%F heidok:30261
%R 10.11588/heidok.00030261
%T Learning by Tooling: Novel Neuromorphic Learning Strategies in Reproducible Software Environments
%U https://archiv.ub.uni-heidelberg.de/volltextserver/30261/
%X Neuromorphic hardware enables novel modes of computation.  We present two innovative learning strategies:  First, we perform spike-based deep learning with LIF neurons in a Time-To-First-Spike coding scheme that focuses on achieving classification results with as few spikes as early as possible.  This is critical for biological agents operating under environmental pressure, requiring quick reflexes while conserving energy.  Deriving exact learning rules, we perform backpropagation on spike-times of LIF neurons in both software and on the BrainScaleS hardware platform.  Second, we present fast energy-efficient analog inference on BrainScaleS-2.  In this non-spiking mode, we use convolutional neural networks to check medical ECG traces for atrial fibrillation.  The newly commissioned BrainScaleS-2 Mobile system has successfully participated and proven to operate reliably in the ``Energy-efficient AI system'' competition held by the German Federal Ministry of Education and Research.  Developing these new computing paradigms from the ground up is a Herculean effort in terms of work required and people involved.  Therefore, we introduce tooling methods to facilitate collaborative scientific software development and deployment.  In particular, we focus on explicitly tracking disjoint sets of software dependencies via Spack, an existing package manager aimed at high performance computing.  They are deployed as monolithic Singularity containers in a rolling-release schedule after thorough verification.  These practices enable us to confidently advance our neuromorphic platform while fostering reproducibility of experiments, a still unsolved problem in software-aided sciences.  By introducing quiggeldy, a micro-scheduling service operating on interleaved experiment-steps by different users, we achieve better hardware interactivity, stability and experiment throughput.