Neuromorphic Engineering I. avlsi.ini.uzh.ch/classwiki. A pidgin vocabulary. Neuromorphic Electronics? What is it all about?
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1 Neuromorphic Engineering I Time and day : Lectures Mondays, 13:15-14:45 Lab exercise location: Institut für Neuroinformatik, Universität Irchel, Y55 G87 Credits: 6 ECTS credit points Exam: Oral minutes at INI Taught by: Tobi Delbruck, Shih-Chii Liu, Giacomo Indiveri Teaching assistants: Yingxue Wang, Rico Moeckel, Raphael Berner avlsi.ini.uzh.ch/classwiki Course schedule Fall 2008 Introduction to neuromorphic avlsi. Arranging lab exercises. Device physics. Transistor properties below threshold Transistor properties above threshold Static analog circuits. Current mirror, diff pair, transconductance amplifier, bump circuit. Linear systems: Follower integrator, follower differentiator. Small signal analysis I. Voltage amplifier. Phototransduction in biological retinas. Adaptive photoreceptor. Small signal analysis II Winner-take-all circuits Neurons in Silicon Synapses, excitatory and inhibitory Long range communication (Address-Event Representation, AER) Logarithmic photosensors Learning in silicon A pidgin vocabulary Neuromorphic Electronics? What is it all about? 1947 Bardeen and Brattain The context (c) T. Delbruck, Inst. of Neuroinformatics 1
2 A finished wafer S G D D G S holes electrons p + p + n + n + n well p well Constant ~$5/cm 2 Artificial real-world computation (or: How industry thinks of analog) ADC Logic DAC The motivation Natural computation Flies acrobatically Recognizes patterns Navigates Forages Communicates J/op Digital silicon 10-7 to J/op 10 8 to 10 4 times as efficient as digital silicon Pentium 4 Computer vs. Brain Anderson et al At the system level, brains are about 1 million times more power efficient than computers. Why? Cost of elementary operation (turning on transistor or activating synapse) is about the same. It s not some magic about physics. Computer Fast global clock Bit-perfect deterministic logical state Memory distant to computation Fast high precision power hungry ADCs Devices frozen on fabrication Brain Cortex Self-timed 1mm Synapses are stochastic! Computation dances: digital analog digital Memory at computation Low precision adaptive data-driven quantizers Constant adaptation and self-modification (c) T. Delbruck, Inst. of Neuroinformatics 2
3 Engineering extremes b φ i A o A history o 1/ b 1 = i 1 1+ b Ab Brains are neither of these! The subterranean group Biophysics of membrane channels Fast forward to 1980 (Caltech) Max Delbruck (Caltech) Moshe Eisenberg (from UPenn) Jim Hall (recalled early from Vietnam) Paul Mueller Peter Leuger (Konstanz) (UPenn) Fred Sigworth Physics of Computation Course History of Neuromorphic Engineering The silicon retina, and all that 1980s 1985 Dick Feynman John Hopfield Misha Mahowald (c) T. Delbruck, Inst. of Neuroinformatics 3
4 ~1990 Rahul Sarpeshkar Lloyd Watts 1989 Andreas Andreou Kwabena Boahen 1967 P. Mueller E. Vittoz C. Mead M. Delbruck CSEM 1980s C. Koch Mid to late 1990s Van Schaik R. Sarpeshkar C. Diorio P. Hasler K. Boahen B. Minch S-C. Liu P.Julian Liu, 2005 The World of Neuromorphic electronics 2006 Types of Neuromorphic chips Silicon retinas electronic models of retinas Silicon cochleas electronic models of cochleas Smart vision chips (e.g. tracking chips, motion sensors, presence sensors) Neural networks of spiking neurons Central pattern generators Models of specific systems: e.g. bat sonar echolocation, lamprey spinal cord for swimming, lobster stomatogastric ganglion, electric fishes Multichip systems that use spikes for interchip communication (c) T. Delbruck, Inst. of Neuroinformatics 4
5 Spinoffs and accomplishments of neuromorphic engineering Mahowald, Douglas, 1991 Mahowald, Mead, 1991 Boahen, 2005 Eric Vittoz Aim of class: To grok these (c) T. Delbruck, Inst. of Neuroinformatics 5
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