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Ulrich Ramacher Corp. Research, Infineon Technologies AG, Munich ulrich.ramacher@infineon

Information Processing with Pulsed Neural Networks. Ulrich Ramacher Corp. Research, Infineon Technologies AG, Munich ulrich.ramacher@infineon.com. Dilemma (1). lack of robustness. Dilemma (2). lack of architecture information. Dilemma (3). Neuron [mV]. Sensor [mV]. Time [ms]. 20µm.

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Ulrich Ramacher Corp. Research, Infineon Technologies AG, Munich ulrich.ramacher@infineon

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  1. Information Processing with Pulsed Neural Networks Ulrich Ramacher Corp. Research, Infineon Technologies AG, Munich ulrich.ramacher@infineon.com

  2. Dilemma (1) lack of robustness

  3. Dilemma (2) lack of architecture information

  4. Dilemma (3) Neuron [mV] Sensor [mV] Time [ms] 20µm • Non-invasive long-term recording of neural tissue. • High-density sensor with 128128 Sensors in 11mm². • Extended CMOS-process with biocompatible high-k surface dielectric. • Self-calibration circuitry and pre-amplification on chip. • Applications in neurobiology and drug discovery Infineon Neurochip Gap between signal processing by few cells and information processing by large arrays

  5. Program • Use pulsed IAF neurons and adaptive synapses (inhibitory, excitatory) • Use experimental evidence • Start from simple, complete vision systems • Find basic network structures for information processing • Find quantitative ansatz for information processing • Demonstrate usefulness for cell phones

  6. or : Neuron Model Rule 1: Reaching the threshold, a neuron resets membrane potential to zero and starts sending a pulse of 1 ms Rule 2: A neuron either receives or sends  deterministic dynamical system

  7. Experiment 1

  8. Experiment 1 Frequency

  9. Observations • „Firing patterns don‘t come to an end“ • information processing is not a function of time  Does frequency of firing patterns characterize the net ? Inet = -pn x ln pn Is entropy a reproducible measure of information?

  10. Experiment 2:

  11. Experiment 2

  12. is a reproducibly measurable quantity Inet = - pn x ln pn Findings • distribution function of fp‘s is independent of initial conditions

  13. Entropy as a function of mean synaptic weight Each color corresponds to a different realization of wij Network size: 40 neurons

  14. Network size: 40 neurons ISI Fire Rate

  15. Synapse Model (proposed by U. Ramacher, April 99) Adaptation rule : local, causal, simple

  16. 20 x 24 pixels , each connected to a neuron by a constant synapse • adaptive synapses connecting neighboured neurons µ negative synapses = coupled system of damped oscillators

  17. 20x24 pixel, 10% noise µ positive synapses = coupled system of damped exponentially rising and falling „elements“

  18. Spot Detector = Illumination Encoder

  19. Pixels: 64 x 64

  20. Projection onto the receptive field of a simple cell in the visual cortex stimulus LGN primary visual cortex (V1) light retina optic nerve Dr. Arne Heittmann

  21. convergence Dr. Arne Heittmann

  22. Stimulus Modeling the Experiment Bright Stimulus Dark Stimulus y0 Dy Dx x0 Stimuli projected onto RF of a Simple Cell P: measured pulse rate Gi: Gabor function H: Spot Intensity B: Background Intensity

  23. i1>i2 i1<i2 Pulse difference detector (1)

  24. Pulse difference detector (2) Dynamics of the Synapse W41:

  25. Pulse difference detector (3) Characteristic Neuron 2 Neuron 1 Number of pulses Neuron 4 i2 i1 = 0.5 Q = 1 WK0 = 0.08 td = 1ms T = 0.5s

  26. 3‘ 3‘ 3‘ 3‘ 4‘ 4‘ 4‘ 4‘ 5‘ 6 Architecture of feature detector(proposed by A. Heittmann, 2003) 1 1 1 1 2 2 2 2 - - - - - - 3 3 3 3 4 4 4 4 5 µ > 0 µ < 0 constant

  27. Shaping the response-profile of the detector section of the retina H: Spot Intensity B: Background Intensity

  28. Results of a detector implementation measured profile Gabor-Wavelet • 256 Gradient detectors • size of receptive field: 17 x 17 Pixel • T=750ms @ 1ms Pulse-duration

  29. Simulated Filter responses, T=750ms ideal Real 90° Imaginary 90° Real 0° Imaginary 0°

  30. The Head-Detector Restriction - single scale (keep eye-distance fixed)

  31. Check for Robustness in Eye-Brow Zone Reference Image Filter Response,horizontal direction region ofinterest 20x20 Pixel new eye-browimage

  32. Detector Layer Memory Layer K K K Input Layer A simple memory (1) , 1 zone Learning Phase 1-1 connection between input and detector layer 1-1 connection between input and memory layer full connection between detector and memory layer

  33. Experiment 1: learned image in input and memory Activity (number of events), 2ms window Pulse-patterns of input-layer

  34. A simple memory (2), 1 Zone Recognition Phase Detector Layer Memory Layer K K 1-1 connection between input and detector layer K full connection between detector and memory layer Gabor Layer

  35. Experiment 3: recognition of non-learned eye-brow Activity (number of events), 2ms window Pulse-patterns of input-layer

  36. Experiment 2: Isolated neurons Activity (number of events), 2ms window Pulse-patterns of input-layer

  37. Zone-Architecture I Detector Layer 1 Detector Layer 2 Memory Layer Vertical Orientations Horizontal Orientations : Gabor-Kernel : Input Image

  38. Zone-Architecture II Zone 5 Zone 1 Zone 6 Zone 2 Zone 7 Zone 3 Zone 4 Zuordnung: Zonen zu Bildregionen : Zone für Gabor-Wavlet mit horizontaler Orientierung : Zone für Gabor-Wavlet mit vertikaler Orientierung

  39. Reference-Image and Test-Images Reference-Image: Test-Images: Face 0011 Face 0014 Face 0001

  40. Face 0001 Activity-Diagram Memory Zone 1 Detector Normalized accumulated activity Memory Zone 2 Detector Memory Zone 3 Detector Memory Zone 4 Detector Memory Zone 5 Detector Time [ms]

  41. Face 0011 Activity-Diagram Memory Zone 1 Detector Normalized accumulated activity Memory Zone 2 Detector Memory Zone 3 Detector Memory Zone 4 Detector Memory Zone 5 Detector Time [ms]

  42. Face 0014 Activity-Diagram Memory Zone 1 Detector Normalized accumulated activity Memory Zone 2 Detector Memory Zone 3 Detector Memory Zone 4 Detector Memory Zone 5 Detector Time [ms]

  43. Binding of zones by synchrony Binding Layer … 1 2 3 4 5 6 7 Zuordnung: Zonen zu Neuronen des Spotdetektors zur Bindung Memory Layer

  44. Results ~ 40ms ~ 40ms Spot_0001 ~ 40ms Spot_0011 Spot_0014

  45. Zone 2 Zone n Zone 1 Column Architecture Image plane Detector Feature 1Memory Feature 1 Detector Feature 2Memory Feature 2 . . . robustrecognition Detector Feature nMemory Feature n ∙ ∙ ∙ Binding Object 1 ∙ ∙ Binding Object 2 AssociativeMemory ∙ ∙ Binding Object n

  46. The vision: a 3D-Vision-Cube The Vision-Cube CMOS-sensor, sensor array • 3D-stacking-architecture • Low-power • Real time capabilities • Integration of sensors and information processing • Distributes layers of information processing to layers of the stack • Solves problem of connectivity Analogue-pulse conversion Feature-Detection Gabor-Wavelets, different orientations … Object-recognition Object-detection

  47. Design of a testchip for the „Synchrony detector“,Base-Chip for the 3D-Stack pixel memory (1 Pixel) Infineon 130nm CMOS-Technology localAER-subcircuit testcircuits for 3D-interconnects Test circuit Integrate-and-Fire-Neuron 3D-vias for supply and digital control signals adaptive synapses layout of 1 neuron rows of 3D-vias for layer-to-layer signaldistribution array of 128 x 128 neurons, 64k synapses control circuit AER-encoder- circuit Power consumption: 3mW @1.5V (analog) , 40-250mW @1.5V (digital)Size: 7.6mm x 7.8mm

  48. Gabor-Feature-Detector chip (layout) including a pulse router for the 3D-integration Infineon 130nm CMOS-Technology Array of 128x128 (64k) processing elements for pulse processing and -routing • Processing element: • photo-Detector • pulse-processing (gradient detection) • dynamic routing of pulses 3D- wiring channel for vertical signal distribution SRAM for storing routing information • Digital macro: • routing circuit • configuration • AER-circuit (event based) 3D- wiring for power supply and control signals

  49. Chip 3 SOLIDConnection Chip 2 15 µm 7 µm Chip 1 14 µm Bottom-Chip 12 µm 3D-Stacking Layer 7 Layer 6 Layer 5 12µm 12µm

  50. Real 3D !! Layer 7 Layer 6 Layer 5 Layer 4 Layer 3 13µm 7µm Layer 2 Bottom Substrate, Layer 1

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