analog vlsi neural circuits cs599 – computational architectures in biological vision
TRANSCRIPT
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!
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?
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.
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
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.
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.