Fast electronic noses through spiking neuromorphic networks
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Fast electronic noses through spiking neuromorphic networks Prof. Thomas Nowotny CCNR, Informatics, Sussex Neuroscience, University of Sussex Efutures Community Event British Museum London, 04-12-2013
description
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. - PowerPoint PPT Presentation
Transcript of Fast electronic noses through spiking neuromorphic networks
Fast electronic noses through spiking neuromorphic networks
Prof. Thomas NowotnyCCNR, Informatics, Sussex Neuroscience,
University of Sussex
Efutures Community EventBritish Museum London, 04-12-2013
The problemEnoses 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
FOX enose
MOx sensors(Figaro)
FOX enose system
O2+Analyte
I
Heater
Substrate
Metal Oxide
The data is theResistance Change
Two different sensor technologies
I
Heater
SNO2
Classical SNO2 Sensors
I
Heater
CTO
The data is theResistance Change
Zeolite-coated CTO Sensors
O2+AnalyteO2+Analyte Zeolite
coating
SubstrateSubstrate
Example data
Hexanol Octenol
Zeolite CTO sensors
SNO2 sensors SNO2 sensors
Zeolite CTO sensors
(here R0 was subtracted)
Time (s)Time (s)
Rela
tive
resp
onse
(au)
Rela
tive
resp
onse
(au)
Faster Features
e.g. A. Z. Berna et al. 2011 ISOEN Conference, New York
R/R0
EMAmax for 3 timescales
Traditional: Steady State
Faster: Transients
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 2013Huerta et al., Neural Computation 2009
Implementation: GeNN GPU Kit and Leicester FPGA Kit
GPU
NVIDIA Tesla
FPGA
Xilinx Virtex
Guerrero-Rivera et al., Programmable Logic Construction Kits for Hyper-Real-TimeNeuronal Modeling. Neural Computation 18, 2651–2679 (2006)
Nowotny et al., GPU enhanced Neuronal Networks (GeNN), BMC Neuroscience 2011, 12(Suppl 1):P239.http://genn.sourceforge.net
Project planWP1: Objective: Develop and verify a spiking network prototype for rapid analysis of chemosensor signals. (month 1-5)Tasks: Implement a GPU‐accelerated spiking network. Tune it for performance on the basis of e‐nose data sets. Benchmark the performance against conventional state‐of‐the‐art approachesOutcome: A GPU‐accelerated spiking network for e‐nose signal analysis.
WP2: Objective: Port the network to neuromorphic hardware. (month 6-7)Tasks: Implement the network using the neuromorphic kit from Leicester (Tim C. Pearce) Verify that the network’s performance on hardware is at level with the software
implementation.Outcome: Hardware implementation of the spiking e‐nose network.
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.
The TeamPIs Consultant
Tim Pearce:Spiking NN on FPGABio-mimetic classification model 3
Russell Binions/ Amalia Berna:Sensor technology and Enose data
Thomas Nowotny:(overall lead)Bio-mimetic classification model 2Spiking NN on GPU
Michael Schmuker:Biomimetic classification model 1Spiking NN on neuromorphic hardware (SPIKEY)
ResearcherInterviews: next week
