Neuromorphic Systems

Bartolozzi, Chiara; Benosman, Ryad; Boahen, Kwabena; Cauwenberghs, Gert; Delbrück, Tobi; Indiveri, Giacomo; Liu, Shih-Chii; Furber, Steve; Imam, Nabil; Linares-Barranco, B.; Serrano-Gotarredona, Teresa; Meier, K.; Posch, C.; Valle, Maurizio

doi:10.1002/047134608x.w8328

Cited by 12 publications

(10 citation statements)

References 142 publications

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“…The BrainScaleS wafer-scale system [21], [28] is built in the first phase with 180 nm CMOS HICANN neuromorphic chips, operated 10 4 -10 5 times faster than biological timescale. The second-generation hardware features the enhanced High Input Count Analog Neural Network with Digital Learning System (HICANN-DLS) chips as the fundamental building block [22].…”

Section: I T H E H I C a N N -D L S C H I Pmentioning

confidence: 99%

An Accelerated LIF Neuronal Network Array for a Large-Scale Mixed-Signal Neuromorphic Architecture

Aamir

Stradmann

Müller

et al. 2018

IEEE Trans. Circuits Syst. I

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We present an array of leaky integrate-and-fire (LIF) neuron circuits designed for the second-generation BrainScaleS mixed-signal 65-nm CMOS neuromorphic hardware. The neuronal array is embedded in the analog network core of a scaleddown prototype HICANN-DLS chip. Designed as continuoustime circuits, the neurons are highly tunable and reconfigurable elements with accelerated dynamics. Each neuron integrates input current from a multitude of incoming synapses and evokes a digital spike event output. The circuit offers a wide tuning range for synaptic and membrane time constants, as well as for refractory periods to cover a number of computational models. We elucidate our design methodology, underlying circuit design, calibration and measurement results from individual subcircuits across multiple dies. The circuit dynamics match with the behavior of the LIF mathematical model. We further demonstrate a winner-take-all network on the prototype chip as a typical element of cortical processing. I . I N T R O D U C T I O NT HE architecture of digital microprocessors is fundamentally different from that of the central nervous system. While the brain is a massively parallel structure of neurons interconnected through synapses [1], microprocessors are mostly based on a von Neumann architecture [2], [3] with logic gates as the elementary primitives. The human brain consumes only approximately 20 W [4], while its performance as a generalpurpose problem solver is still unmatched by any computer algorithm.Taking inspiration from this biological feat, neuromorphic architectures not only adopt a non-von Neumann architecture by collocating memory close to the computational element, but also introduce massive parallelism, high energy efficiency, reconfigurability, fault tolerance, and integrate computational *Both authors contributed equally to this work.

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Section: I T H E H I C a N N -D L S C H I Pmentioning

confidence: 99%

An Accelerated LIF Neuronal Network Array for a Large-Scale Mixed-Signal Neuromorphic Architecture

Aamir

Stradmann

Müller

et al. 2018

IEEE Trans. Circuits Syst. I

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“…This ranges from the development of biologically inspired circuits like the adaptive exponential I&F neuron 43,44 and their integration in spiking deep neural networks 45 to mixed signal circuits which exploit events for processing visual information [46][47][48][49] . For a review on other neuromorphic systems and circuits, as well as mimicking other senses, the reader can refer to Bartolozzi et al 50 .…”

Section: A Spiking and Adapting Tactile Sensor For Neuromorphic Applimentioning

confidence: 99%

A spiking and adapting tactile sensor for neuromorphic applications

Birkoben

Winterfeld

Fichtner

et al. 2020

Sci Rep

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The ongoing research on and development of increasingly intelligent artificial systems propels the need for bio inspired pressure sensitive spiking circuits. Here we present an adapting and spiking tactile sensor, based on a neuronal model and a piezoelectric field-effect transistor (PiezoFET). The piezoelectric sensor device consists of a metal-oxide semiconductor field-effect transistor comprising a piezoelectric aluminium-scandium-nitride (AlxSc1−xN) layer inside of the gate stack. The so augmented device is sensitive to mechanical stress. In combination with an analogue circuit, this sensor unit is capable of encoding the mechanical quantity into a series of spikes with an ongoing adaptation of the output frequency. This allows for a broad application in the context of robotic and neuromorphic systems, since it enables said systems to receive information from the surrounding environment and provide encoded spike trains for neuromorphic hardware. We present numerical and experimental results on this spiking and adapting tactile sensor.

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“…The limitations of traditional approaches, especially in applications which need to handle a large amount of information within short periods, triggered the development of spike-based bio-inspired and neuromorphic systems to mirror the incredibly high information handling efficiency of biological systems. These neuromorphic systems employ spike-based information representation principles in sensing, communication, and processing [30,31,36,37,[50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68] and report major improvements in efficiency and speed, that is, the systems require less power, and handle information with higher temporal resolution and less latency. Spike-based neuromorphic systems exploit all neural codes found in biology and are realized on customized hardware with special asynchronous circuits, optimized for spike-based signals and processing, and mimicking neural computation principles.…”

Section: Efficient Event-driven Information Handling For Large-area Skin Systemsmentioning

confidence: 99%

“…At the beginning of Section 3, we outlined that many works introduced spike-based neuromorphic systems mimicking the information representation and neural computation principles found in nature [30,31,36,37,[50][51][52][53][54][55][56][57][58][58][59][60][61][62][63][64][65][66][67][68]. Since these systems are spike-driven, it is valid to describe them as event-driven systems (EDSs).…”

Section: Event-driven Systems (Eds)mentioning

confidence: 99%

Design and Realization of an Efficient Large-Area Event-Driven E-Skin

Bergner

Dean-León

Cheng

2020

Sensors

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The sense of touch enables us to safely interact and control our contacts with our surroundings. Many technical systems and applications could profit from a similar type of sense. Yet, despite the emergence of e-skin systems covering more extensive areas, large-area realizations of e-skin effectively boosting applications are still rare. Recent advancements have improved the deployability and robustness of e-skin systems laying the basis for their scalability. However, the upscaling of e-skin systems introduces yet another challenge—the challenge of handling a large amount of heterogeneous tactile information with complex spatial relations between sensing points. We targeted this challenge and proposed an event-driven approach for large-area skin systems. While our previous works focused on the implementation and the experimental validation of the approach, this work now provides the consolidated foundations for realizing, designing, and understanding large-area event-driven e-skin systems for effective applications. This work homogenizes the different perspectives on event-driven systems and assesses the applicability of existing event-driven implementations in large-area skin systems. Additionally, we provide novel guidelines for tuning the novelty-threshold of event generators. Overall, this work develops a systematic approach towards realizing a flexible event-driven information handling system on standard computer systems for large-scale e-skin with detailed descriptions on the effective design of event generators and decoders. All designs and guidelines are validated by outlining their impacts on our implementations, and by consolidating various experimental results. The resulting system design for e-skin systems is scalable, efficient, flexible, and capable of handling large amounts of information without customized hardware. The system provides the feasibility of complex large-area tactile applications, for instance in robotics.

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Neuromorphic Systems

Cited by 12 publications

References 142 publications

An Accelerated LIF Neuronal Network Array for a Large-Scale Mixed-Signal Neuromorphic Architecture

An Accelerated LIF Neuronal Network Array for a Large-Scale Mixed-Signal Neuromorphic Architecture

A spiking and adapting tactile sensor for neuromorphic applications

Design and Realization of an Efficient Large-Area Event-Driven E-Skin

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