Analog VLSI Neural Circuits

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Analog VLSI Neural Circuits CS599 – computational architectures in biological vision

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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! - PowerPoint PPT Presentation

Transcript of Analog VLSI Neural Circuits

Page 1: Analog VLSI Neural Circuits

Analog VLSI Neural Circuits

CS599 – computational architectures in biological

vision

Page 2: Analog VLSI Neural Circuits

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

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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!

http://www.ic-media.com/
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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?

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A VLSI MOS transistor

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An analog chip layout: the wish

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An actual chip: the cold reality

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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.

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Remember? Electron Micrograph of a Real Neuron

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

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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

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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.

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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.

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Full network

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VLSI sensor with retinal organization

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Carver Mead: the floating gate

www.cs.washington.edu/homes/hsud/fg_workshop.html

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Spatial layout

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Electron tunneling

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Electron tunneling

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Hot electron injection

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Hot electron injection

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Spatial layout

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A learning synapse circuit


Faults in Vlsi Circuits

Faults in Vlsi Circuits

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

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

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Implementing neural architectures using analog …IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, VOL. 36, NO.5, MAY 1989 643 Implementing Neural Architectures Using Analog VLSI Circuits

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