TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip

Akopyan, Filipp; Sawada, Jun; Cassidy, Andrew; Alvarez-Icaza, Rodrigo; Arthur, John V.; Merolla, Paul; Imam, Nabil; Nakamura, Yutaka; Datta, Pallab; Nam, Gi-Joon; Taba, Brian; Beakes, Michael P.; Brezzo, Bernard; Kuang, J.B.; Manohar, Rajit; Risk, W. P.; Jackson, Bryan L.; Modha, Dharmendra S.

doi:10.1109/tcad.2015.2474396

Cited by 1,054 publications

(547 citation statements)

References 35 publications

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“…The resolution required for effective weighting is a topic of debate within the neuromorphic electronics community, with IBM's TrueNorth selecting four digital bits plus one sign bit [111]. In Refs.…”

Section: Single-channel Control Accuracy and Precisionmentioning

confidence: 99%

“…Initial demonstrations achieved simultaneous four-channel MRR weight control with an accuracy of 3.8 bits and precision of 4.0 bits (plus 1.0 sign bit) on each channel (Figure 9). Although optimal weight resolution is still a topic of discussion in the neuromorphic electronics community [9], several state-of-the-art architectures with dedicated weight hardware have settled on 4-bit resolution [111,115].…”

Section: Multichannel Control Accuracy and Precisionmentioning

confidence: 99%

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Progress in neuromorphic photonics

et al. 2017

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As society's appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. Recently, the landscape for commercially manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications.

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Section: Single-channel Control Accuracy and Precisionmentioning

confidence: 99%

Section: Multichannel Control Accuracy and Precisionmentioning

confidence: 99%

Progress in neuromorphic photonics

et al. 2017

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“…It has been a long-held belief that a biological computing model inspired from the human brain may solve the challenges that the von Neumann architecture faces [3]. The VLSI realization of such a neuro-biological architecture, namely, neuromorphic computing, has recently been revitalized by the application of emerging devices [4].…”

Section: List Of Tablesmentioning

confidence: 99%

A quantization-aware regularized learning method in multilevel memristor-based neuromorphic computing system

Song

Liu

Wen

et al. 2017

2017 IEEE 6th Non-Volatile Memory Systems and Applications Symposium (NVMSA)

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“…The operation of each core is based on the digital computation of spiking neurons, and spike events are carried throughout the network using time-multiplexed wires that connect a mesh network of routers. Each chip consumes 65 mW, [98]. The primary application has been determined as multiobject detection from a video input, [99].…”

Section: Large-scale Realizationsmentioning

confidence: 99%

Neuromorphic microelectronics from devices to hardware systems and applications

Schmid

2016

NOLTA

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Neuromorphic systems aiming at mimicking some characteristics of the nervous systems of living humans or animals have been developed since the late 1980s', taking benefit of intrinsic properties and increasing performances of the successive silicon fabrication technologies. A regain of interest has been observed in the middle of the 2010s', which manifests itself from the emergence of large-scale projects integrating various computational and hardware perspectives, by the increased interest and involvement of industry and the growth of the volume of scientific publications. This paper reviews research directions and methods of neuromorphic microelectronics hardware, the developed hardware and its performance, and discusses current issues and potential future developments.

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TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip

Cited by 1,054 publications

References 35 publications

Progress in neuromorphic photonics

Progress in neuromorphic photonics

A quantization-aware regularized learning method in multilevel memristor-based neuromorphic computing system

Neuromorphic microelectronics from devices to hardware systems and applications

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