Neuromorphic silicon chips

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Presentation transcript:

Neuromorphic silicon chips

Who we are iniLabs is a spin-off company which commercialises technology from the Institute of Neuroinformatics (INI) at the University of Zurich and ETH Zurich INI has researched Neuromorphic Engineering since 1990s Prof. Giacomo Indiveri could not be here today

Neuromorphic engineering Designing electronic circuits which behave similarly to elements of biological nervous systems May help us understand how the nervous system works May help us to build better computers I'll present a brief overview, highlighting design choices

Neuromorphic engineering Antecedents Mechanical automata (18th-19th century) e.g. Vaucanson's digesting duck: Cybernetic robots (1950s) e.g. Grey Walter's Turtle:

Neuromorphic engineering Antecedents Neural electrical circuits (1960s-1970s) e.g. Fukushima and Kuffler 1970: An Electronic model of the retina Hopfield 1984: An electronic model of neural associative memory

Silicon integrated circuits Mead and Mahowald 1988 “A silicon model of early visual processing” Models have been made of: Retinae Cochleae Cognition in central nervous system

Parallelism Many simple imperfect elements acting simultaneously Many connections between units http://www.rsipvision.com/wp-content/uploads/2015/04/Slide5.png Image by S. Ramon y Cajal

Spikes Artificial neural networks – synchronous layered approach Hopfield network – continuous operation Real neurons communicate with pulses (spikes) Relevance of spike timing Bi and Poo 1998: “Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type”

Spikes Asynchronous pulse-based communication Address-Event Representation Multiplexing uses speed difference between electronic and ionic transmission Image by S. Ramon y Cajal

Many to many connectivity Fan in and fan out (order 10^5 synapses converge on cells in the cerebellum)

Analogue vs digital neurons Indiveri et al 2006 Analogue neurons and synapses One circuit per neuron/synapse “Subthreshold” operation c.f.: IFAT (Cauwenberghs, UCSD) True North (Modha, IBM) Spinnaker (Furber, Manchester)

Neural prosthesis Vogelstein et al 2008 ”A Silicon Central Pattern Generator Controls Locomotion in Vivo”

Spike-based deep nets O'Connor et al. 2013 "Real-time classification and sensor fusion with a spiking deep belief network."

Learning in silicon Giulioni et al. 2015 "Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems"

The promise Attempting to model the nervous system in hardware helps us to understand the nervous system gives us insights into how to solve (computational) problems Low-power, real-time, real-world computation

Applicability? CERN experiments are not continuous but pulsed Multiplexing of connections exploits difference between electronic and ionic speeds; when attempting to react much faster than biological reactions this ceases to be an advantage No serious power constraints Could CERN benefit from any of the following? Dedicated computational hardware implementing massive parallelism? Pulse-timing based transmission of (sensor) values? Recurrency in classification? Continuous learning (supervised/unsupervised)?

Neuromorphic engineering Gutig and Sompolinsky 2006 “The Tempotron: a neuron that learns spike-timing based decisions”

Dynamic Vision Sensor (DVS)