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Analog VLSI Neural Circuits

Analog VLSI Neural Circuits. CS599 – computational architectures in biological vision. Charge-Coupled Devices. Uniform array of sensors Very little on-board processing Very inexpensive. CMOS devices. More onboard processing Even cheaper!

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Analog VLSI Neural Circuits

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  1. Analog VLSI Neural Circuits CS599 – computational architectures in biological vision

  2. Charge-Coupled Devices • Uniform array of sensors • Very little on-board processing • Very inexpensive

  3. CMOS devices • More onboard processing • Even cheaper! • Example: ICM532B from www.ic-media.com: single-chip solution includes photoreceptor array, various gain control and color adjustment mechanisms, image compression and USB interface. Just add a lens and provide power!

  4. The challenge • Digital processing is power hungry • Analog processing is much more energy efficient • But … so much variability in the gain of transistors obtained when fabricating highly integrated (VLSI) chips that analog computations seem impossible: nearly each analog amplifier on the chip should be associated with control pins, analog memories, etc to correct for fabrication variability. Hopeless situation?

  5. A VLSI MOS transistor

  6. An analog chip layout: the wish

  7. An actual chip: the cold reality

  8. Biological motivation • Well, there is also a lot of variability in size and shape of neurons from a same class • But the brain still manages to produce somewhat accurate computations • What’s the trick? online adaptability to counteract morphological and electrical mismatches among elementary components.

  9. Remember? Electron Micrograph of a Real Neuron

  10. Mahowald & Mead’s Silicon Retina • Smoothing network: allows system to adapt to various light levels.

  11. Andreou and Boahen's silicon retina See http://www.iee.et.tu-dresden.de/iee/eb/ analog/papers/mirror/visionchips/vision_chips/ andreou_retina.html

  12. Diffusive network • dQn/dt is the current supplied by the network to node n, and D is the diffusion constant of the network, which depends on the transistor parameters, and the voltage Vc.

  13. Full network • Two layers of the diffusive network: upper corresponds to horizontal cells in retina and lower to cones. Horizontal N-channel transistors model chemical synapses. • The function of the network can be approximated by the biharmonic equation where g and h are proportional to the diffusivity of the upper and lower smoothing layers, respectively.

  14. Full network

  15. VLSI sensor with retinal organization

  16. Carver Mead: the floating gate www.cs.washington.edu/homes/hsud/fg_workshop.html

  17. Spatial layout

  18. Electron tunneling

  19. Electron tunneling

  20. Hot electron injection

  21. Hot electron injection

  22. Spatial layout

  23. A learning synapse circuit

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