Towards artificial general intelligence with hybrid Tianjic chip architecture

Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie; Yang, Zheyu; Ma, Cheng; Li, Guoqi; Han, Wentao; Li, Huanglong; Wu, Huaqiang; Zhao, Rong; Xie, Yuan

doi:10.1038/s41586-019-1424-8

Cited by 635 publications

(423 citation statements)

References 41 publications

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Section: Current State Of Memristive Systems For Neuromorphic Computingmentioning

confidence: 99%

“…By combining the strengths of modern VLSI technology and mimicking the principle of the biological brain, neuromorphic machines are potentially more powerful in managing high‐dimensionality and unstructured data while operating with much lower power consumption. Currently, commercial neuromorphic systems are mostly built on complementary CMOS circuits, including TrueNorth by IBM, SpiNNaker by the University of Manchester, Neurogrid by Stanford University, Loihi by Intel, and Tianjic by Tsinghua University . Compared to conventional processors based on Von Neumann architecture, these neuromorphic chips exhibit an exceptional information processing capability at much lower power consumption levels, especially for unstructured data (Figure d).…”

Section: Current State Of Memristive Systems For Neuromorphic Computingmentioning

confidence: 99%

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Artificial Perception Built on Memristive System: Visual, Auditory, and Tactile Sensations

Zhao

Tan

et al. 2020

Advanced Intelligent Systems

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The widespread implementation and rapid development of autonomous systems pose stringent performance requirements on emerging sensory systems. In addition to the basic sensing requirements, leading sensory systems are required to process data and extract featured information from highly redundant data in real time. With the added edge‐computational capabilities, data shuttling is avoided, leading to significant reduction of computational burden and bandwidth pressure in the cloud. Among the different computing architectures, the neuromorphic sensory system stands out due to its high power efficiency, low latency, and excellent processing capability. Mimicking the biological neural network, the colocation of sensory, processor, and memory components of neuromorphic sensory systems enables the requirements for frequent data shuttles to be circumvented. In particular, artificial intelligent perceptions built on memristive neuromorphic systems exhibit outstanding characteristics of small footprint, low power consumption, 3D stacking ability, and high density. Herein, the two essential parts of the memristive artificial perceptron system are presented: 1) memristive systems for neuromorphic computing and 2) high‐performance sensors. Next, the current state of the art established on artificial perceptron systems covering visual, auditory, and tactile sensations is highlighted. To conclude, the current challenges and future direction in the area of advanced intelligent perceptions are presented.

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Section: Current State Of Memristive Systems For Neuromorphic Computingmentioning

confidence: 99%

Section: Current State Of Memristive Systems For Neuromorphic Computingmentioning

confidence: 99%

Artificial Perception Built on Memristive System: Visual, Auditory, and Tactile Sensations

Zhao

Tan

et al. 2020

Advanced Intelligent Systems

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“…In this work, we propose a hybrid neural network that incorporates both computer-science-and neuroscience-oriented models, as in recent work [17], [18], but for VPR tasks for the first time 1 . Our approach comprises two key components (see Fig.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Compact Neural Architecture for Visual Place Recognition

Chancán

Hernandez-Nuñez

Narendra

et al. 2020

IEEE Robot. Autom. Lett.

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State-of-the-art algorithms for visual place recognition, and related visual navigation systems, can be broadly split into two categories: computer-science-oriented models including deep learning or image retrieval based techniques with minimal biological plausibility, and neuroscience-oriented dynamical networks that model temporal properties found in neural cells underlying spatial navigation in the brain. In this paper, we propose a new compact and high-performing place recognition hybrid model that bridges this divide for the first time. Our approach comprises two key components that incorporate neural models of these two categories: (1) FlyNet, a compact, sparse twolayer neural network inspired by brain architectures of fruit flies, Drosophila melanogaster, and (2) a one-dimensional continuous attractor neural network (CANN). The resulting FlyNet+CANN network combines the compact pattern recognition capabilities of our FlyNet model with the powerful temporal filtering capabilities of an equally compact CANN, replicating entirely in a hybrid neural implementation the functionality that yields high performance in algorithmic localization approaches like SeqSLAM. We evaluate our approach, and compare it to three state-of-the-art place recognition methods, on two benchmark real-world datasets with small viewpoint variations and extreme environmental changes; including day/night cycles where it achieves an AUC performance of 87% compared to 60% for Multi-Process Fusion, 46% for LoST-X and 1% for SeqSLAM, while being 6.5, 310, and 1.5 times faster respectively.

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“…The artificial neural network, a biologically inspired computing technology (e.g., deep learning), has had a tremendous influence on science and technology as well as our daily lives . However, state‐of‐the‐art techniques based on artificial neural networks are running with traditional computers based on the von Neumann architecture, leading to long‐processing latency and high energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Synaptic Devices Based on 2D Materials

Sun

Wang

Yang

2020

Advanced Intelligent Systems

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Diverse synaptic plasticity with a wide range of timescales in biological synapses plays an important role in memory, learning, and various signal processing with exceptionally low power consumption. Emulating biological synaptic functions by electric devices for neuromorphic computation has been considered as a way to overcome the traditional von Neumann architecture in which separated memory and information processing units require high power consumption for their functions. Synaptic devices are expected to conduct complex signal processing such as image classification, decision‐making, and pattern recognition in artificial neural networks. Among various materials and device architectures for synaptic devices, 2D materials and their van der Waals (vdW) heterostructures have been attracting tremendous attention from researchers based on their capacity to mimic unique synaptic plasticity for neuromorphic computing. Herein, the basic operations of biological synapses and physical properties of 2D materials are discussed, and then 2D materials and their vdW heterostructures for advanced synaptic operations with novel working mechanisms are reviewed. In particular, there is a focus on how to design synaptic devices with the vdW structures in terms of critical 2D materials and their limitations, providing insight into the emerging synaptic device systems and artificial neural networks with 2D materials.

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Towards artificial general intelligence with hybrid Tianjic chip architecture

Cited by 635 publications

References 41 publications

Artificial Perception Built on Memristive System: Visual, Auditory, and Tactile Sensations

Artificial Perception Built on Memristive System: Visual, Auditory, and Tactile Sensations

A Hybrid Compact Neural Architecture for Visual Place Recognition

Recent Progress in Synaptic Devices Based on 2D Materials

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