A Re-configurable On-line Learning Spiking Neuromorphic Processor comprising 256 neurons and 128K synapses (bibtex)
by , , , , , ,
Abstract:
Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm 2 , and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.
Reference:
A Re-configurable On-line Learning Spiking Neuromorphic Processor comprising 256 neurons and 128K synapses (Ning Qiao, Hesham Mostafa, Federico Corradi, Marc Osswald, Fabio Stefanini, Dora Sumislawska, Giacomo Indiveri), In Frontiers in Neuroscience, volume 9, 2015.
Bibtex Entry:
@Article{Qiao_etal15,
author		= {Qiao, Ning and Mostafa, Hesham and Corradi, Federico and
		  Osswald, Marc and Stefanini, Fabio and Sumislawska, Dora
		  and Indiveri, Giacomo},
title		= {A Re-configurable On-line Learning Spiking Neuromorphic
		  Processor comprising 256 neurons and 128K synapses},
journal		= {Frontiers in Neuroscience},
year		= {2015},
volume		= {9},
number		= {141},
issn		= {1662-453X},
doi		= {10.3389/fnins.2015.00141},
url		= {http://www.frontiersin.org/neuromorphic_engineering/10.3389/fnins.2015.00141/abstract}
		  ,
abstract	= {Implementing compact, low-power artificial neural
		  processing systems with real-time on-line learning
		  abilities is still an open challenge. In this paper we
		  present a full-custom mixed-signal VLSI device with
		  neuromorphic learning circuits that emulate the biophysics
		  of real spiking neurons and dynamic synapses for exploring
		  the properties of computational neuroscience models and for
		  building brain-inspired computing systems. The proposed
		  architecture allows the on-chip configuration of a wide
		  range of network connectivities, including recurrent and
		  deep networks with short-term and long-term plasticity. The
		  device comprises 128 K analog synapse and 256 neuron
		  circuits with biologically plausible dynamics and bi-stable
		  spike-based plasticity mechanisms that endow it with
		  on-line learning abilities. In addition to the analog
		  circuits, the device comprises also asynchronous digital
		  logic circuits for setting different synapse and neuron
		  properties as well as different network configurations.
		  This prototype device, fabricated using a 180 nm 1P6M CMOS
		  process, occupies an area of 51.4 mm 2 , and consumes
		  approximately 4 mW for typical experiments, for example
		  involving attractor networks. Here we describe the details
		  of the overall architecture and of the individual circuits
		  and present experimental results that showcase its
		  potential. By supporting a wide range of cortical-like
		  computational modules comprising plasticity mechanisms,
		  this device will enable the realization of intelligent
		  autonomous systems with on-line learning capabilities.}
}
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