A 128 X 128 120db 30mw asynchronous vision sensor that responds to relative intensity change

Lichtsteiner, P.; Posch, C.; Delbrück, Tobi

doi:10.1109/isscc.2006.1696265

Cited by 255 publications

(233 citation statements)

References 8 publications

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“…The multichip agent is composed of five multineuron chips (20,21) and a silicon retina (22). The sensor and chips communicate by action-potential-like address events (23).…”

Section: Resultsmentioning

confidence: 99%

“…Real-time neuromorphic agent able to perform the context-dependent visual task. Two moving oriented bars are shown to an event-based 128 × 128 "silicon retina" (22). The silicon retina output events are preprocessed in software to detect orientation and routed accordingly to one of two possible feature maps, implemented as 32 × 32 sheets of VLSI I&F neurons.…”

Section: Resultsmentioning

confidence: 99%

“…The bars move diagonally across the screen and rebound off its borders. The vision sensor generates action potential-like address events when and where its pixels detect local changes in contrast (22) due to the motion of the bars. These asynchronous address events are processed by real-time software (24) that detects local stimulus orientation and assigns a vertical or horizontal orientation label to each event.…”

Section: Resultsmentioning

confidence: 99%

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Synthesizing cognition in neuromorphic electronic systems

Neftci

Binas

Rutishauser

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

127

122

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The quest to implement intelligent processing in electronic neuromorphic systems lacks methods for achieving reliable behavioral dynamics on substrates of inherently imprecise and noisy neurons. Here we report a solution to this problem that involves first mapping an unreliable hardware layer of spiking silicon neurons into an abstract computational layer composed of generic reliable subnetworks of model neurons and then composing the target behavioral dynamics as a "soft state machine" running on these reliable subnets. In the first step, the neural networks of the abstract layer are realized on the hardware substrate by mapping the neuron circuit bias voltages to the model parameters. This mapping is obtained by an automatic method in which the electronic circuit biases are calibrated against the model parameters by a series of population activity measurements. The abstract computational layer is formed by configuring neural networks as generic soft winner-take-all subnetworks that provide reliable processing by virtue of their active gain, signal restoration, and multistability. The necessary states and transitions of the desired high-level behavior are then easily embedded in the computational layer by introducing only sparse connections between some neurons of the various subnets. We demonstrate this synthesis method for a neuromorphic sensory agent that performs real-time context-dependent classification of motion patterns observed by a silicon retina.decision making | sensorimotor | working memory | analog very large-scale integration | artificial neural systems U nlike digital simulations, in which the dynamics of neuronal models are encoded and calculated on general purpose digital hardware, "neuromorphic" emulations express the dynamics of the neural systems directly on an analogous physical substrate (1). Digital simulations have the advantage that they can be exactly and reliably programmed using numerical operations of very high precision. However, they suffer the disadvantage that they are cast on abstract binary electronic circuits whose operation is entirely divorced from the physical processes being simulated. Consequently, such simulations do not readily advance our understanding of how biological neural systems are able to attain their extraordinary physical performance, using only large numbers of apparently unreliable, slow, and imprecise neural components, a problem first recognized by von Neumann more than a half century ago (2). Furthermore, the reliability of digital systems comes at high cost of the circuit complexity necessary for the orchestration of communication and processing, an overhead that declares itself also in the costs of system construction and power dissipation (3).The alternative, neuromorphic, approach to information processing strives to capture in complementary metal-oxide semiconductor (CMOS) very large-scale integration (VLSI) electronic technology the more distributed, asynchronous, and limited precision nature of biological intelligent systems (1, 4).†, ‡ Research...

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“…The multichip agent is composed of five multineuron chips (20,21) and a silicon retina (22). The sensor and chips communicate by action-potential-like address events (23).…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Synthesizing cognition in neuromorphic electronic systems

Neftci

Binas

Rutishauser

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

127

122

View full text Add to dashboard Cite

show abstract

“…A piece of hardware referred to Silicon Retina(SR) was made to convert input streams of image frames into spike patterns in a format referred to Address Encoding Representation (AER) [8]. The pixels of the SR respond to events that represent relative changes in intensity by computing the difference in pixel intensity between two successive frames (temporal contrast) and generating spikes if this difference exceeds a threshold value.…”

Section: Image To Spike Codingmentioning

confidence: 99%

Evaluating SPAN Incremental Learning for Handwritten Digit Recognition

Mohemmed

Kasabov

2012

Neural Information Processing

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In a previous work [12,11], the authors proposed SPAN: a learning algorithm based on temporal coding for Spiking Neural Network (SNN). The algorithm trains a neuron to associate target spike patterns to input spatio-temporal spike patterns. In this paper we present the details of experiment to evaluate the feasibility of SPAN learning on a real-world dataset: classifying images of handwritten digits. As spike encoding is an important issue in using SNN for practical applications, we discuss few methods for image conversion to spike patterns. The experiment yields encouraging results to consider the SPAN learning for practical temporal pattern recognition applications. Abstract. In a previous work [12,11], the authors proposed SPAN: a learning algorithm based on temporal coding for Spiking Neural Network (SNN). The algorithm trains a neuron to associate target spike patterns to input spatio-temporal spike patterns. In this paper we present the details of experiment to evaluate the feasibility of SPAN learning on a real-world dataset: classifying images of handwritten digits. As spike encoding is an important issue in using SNN for practical applications, we discuss few methods for image conversion to spike patterns. The experiment yields encouraging results to consider the SPAN learning for practical temporal pattern recognition applications.

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“…The functionality of the USBAERmini2 board was evolved from the monitoring capability in [8] to include sequencing. The number of jumpers is minimized by autodetection of connected devices.…”

Section: Introductionmentioning

confidence: 99%

A 5 Meps $100 USB2.0 Address-Event Monitor-Sequencer Interface

Berner

Delbrück

Balcells

et al. 2007

2007 IEEE International Symposium on Circuits and Systems (ISCAS)

Self Cite

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Abstract-This paper describes a high-speed USB2.0 AddressEvent Representation (AER) interface that allows simultaneous monitoring and sequencing of precisely timed AER data. This low-cost (<$100), two chip, bus powered interface can achieve sustained AER event rates of 5 megaevents per second (Meps). Several boards can be electrically synchronized, allowing simultaneous synchronized capture from multiple devices. It has three AER ports, one for sequencing, one for monitoring and one for passing through the monitored events. This paper also describes the host software infrastructure that makes the board usable for a heterogeneous mixture of AER devices and that allows recording and playback of recorded data.

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A 128 X 128 120db 30mw asynchronous vision sensor that responds to relative intensity change

Cited by 255 publications

References 8 publications

Synthesizing cognition in neuromorphic electronic systems

Synthesizing cognition in neuromorphic electronic systems

Evaluating SPAN Incremental Learning for Handwritten Digit Recognition

A 5 Meps $100 USB2.0 Address-Event Monitor-Sequencer Interface

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