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Fast electronic noses through spiking neuromorphic networks

Fast electronic noses through spiking neuromorphic networks. Efutures Community Event British Museum London , 04-12 -2013. Prof. Thomas Nowotny CCNR, Informatics, Sussex Neuroscience , University of Sussex. The problem. Enoses are slow.

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Fast electronic noses through spiking neuromorphic networks

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  1. Fastelectronic noses through spiking neuromorphic networks Efutures Community Event British Museum London, 04-12-2013 Prof. Thomas Nowotny CCNR, Informatics,Sussex Neuroscience, University of Sussex

  2. The problem Enoses are slow Chemical sensors are much slower than animals’ sensors • The analysis of sensor data is “slow”: • Based on entire measurement • Done “offline” • Animals make decisions long before their receptors reach equilibrium • Decisions are made “online” with a continuous input stream • Use biomimetic spiking neural networks • Simulating SNN is slow(ish) • Use neuromorphic hardware to accelerate to hyper-realtime

  3. FOX enose MOx sensors(Figaro) O2+Analyte Metal Oxide I FOX enose system Substrate The data is theResistance Change Heater

  4. Two different sensor technologies Classical SNO2 Sensors Zeolite-coated CTO Sensors O2+Analyte Zeolitecoating O2+Analyte SNO2 CTO I I Substrate Substrate The data is theResistance Change Heater Heater

  5. Example data Hexanol Octenol ZeoliteCTOsensors Relative response (au) Relative response (au) ZeoliteCTOsensors SNO2 sensors SNO2 sensors Time (s) Time (s) (here R0 was subtracted)

  6. Faster Features Traditional: Steady State R/R0 Faster: Transients EMAmaxfor 3 timescales e.g. A. Z. Berna et al. 2011 ISOEN Conference, New York

  7. Traditional approach: • Measure steady states activation • Use discriminant analysis and/or machine learning methods Bio-mimetic online approach: • Use spiking neural network • Make “guesses” continuously in real time • Use neuromorphic systems to make this viable Models: Pfeil et al., Frontiers in Neuroscience 2013 Huerta et al., Neural Computation 2009

  8. Implementation: GeNN GPU Kit and Leicester FPGA Kit GPU FPGA NVIDIA Tesla Xilinx Virtex Nowotny et al., GPU enhanced Neuronal Networks (GeNN), BMC Neuroscience 2011, 12(Suppl 1):P239.http://genn.sourceforge.net Guerrero-Rivera et al., Programmable Logic Construction Kits for Hyper-Real-Time Neuronal Modeling. NeuralComputation 18, 2651–2679 (2006)

  9. Project plan WP1: Objective: Developandverifyaspikingnetworkprototypeforrapidanalysisof chemosensorsignals. (month 1-5) Tasks: • Implement aGPU‐accelerated spikingnetwork. • Tuneitforperformanceonthebasisofe‐nose datasets. • Benchmarktheperformanceagainstconventionalstate‐of‐the‐art approaches Outcome: AGPU‐accelerated spikingnetworkfore‐nose signalanalysis. WP2: Objective: Portthenetworktoneuromorphichardware. (month 6-7) Tasks: Implement thenetworkusingtheneuromorphickitfromLeicester (TimC. Pearce) • Verifythatthenetwork’sperformanceonhardwareisatlevelwiththesoftwareimplementation. Outcome: Hardwareimplementationofthespikinge‐nose network.

  10. Future Perspectives • Porting to SPIKEY (Karlheinz Meier, Heidelberg) • Scaling the classifiers to HiCANN and wafer-size system • Exploring implementations for SpiNNaker(Steve Furber, Manchester) M Schmuker has 2 year Marie Curie Fellowship from September 2014. We (M Schmuker & T Nowotny) have applied for HBP funding to further pursue this.

  11. The Team PIs Consultant Tim Pearce: Spiking NN on FPGA Bio-mimetic classification model 3 Michael Schmuker: Biomimetic classification model 1 Spiking NN on neuromorphic hardware (SPIKEY) Russell Binions/ AmaliaBerna: Sensor technology and Enose data Researcher Thomas Nowotny: (overall lead) Bio-mimetic classification model 2 Spiking NN on GPU Interviews: next week

  12. Acknowledgments More info: email t.nowotny@sussex.ac.uk

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