Hardware design and the competency awareness of a neural network

Ding, Yan; Jiang, Weiwen; Lou, Qiuwen; Liu, Jinglan; Xiong, Jinjun; Hu, Xiaobo Sharon; Xu, Xiaowei; Shi, Yiyu

doi:10.1038/s41928-020-00476-7

Cited by 15 publications

(7 citation statements)

References 79 publications

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“…In this context, the significance of adopting a more interactive approach has gradually been realized and a growing number of enticingly new neuromorphic functions have been being demonstrated by exploiting the intrinsic hardware nature for brain‐like neural networks. [ 14,509,790,825,877,913,964–968 ] Communication and codesign across these different levels will fully unleash the potential of hardware implementations.…”

Section: Discussionmentioning

confidence: 99%

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo

Zou

et al. 2021

Advanced Intelligent Systems

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Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic electronic systems are complex, having multiple levels of organization. Inspired by David Marr's famous three‐level analytical framework developed for neuroscience, the advances in neuromorphic electronic systems are selectively surveyed and given significance to these research endeavors as appropriate from the computational level, algorithmic level, or implementation level. Under this framework, the problem of how to build a neuromorphic electronic system is defined in a tractable way. In conclusion, the development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low‐level knowledge (implementations and algorithms) to achieve high‐level brain‐like (human‐level) computational functions. An opportunity arises from the communication among different levels and their codesign. Neuroscience lab‐on‐neuromorphic chip platforms offer additional opportunity for mutual benefit between the two disciplines.

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Section: Discussionmentioning

confidence: 99%

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo

Zou

et al. 2021

Advanced Intelligent Systems

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“…e human capital theory emphasizes the important role played by human resources in economic growth and social progress and believes that human capital is more influential than physical capital, while human investment is an important means to improve the quality of the population and promote economic development. Ding and others pointed out that capable people are the key to modern economic abundance and that they are an important source of economic growth [14]. Migdał-Najman et al also conducted relevant research on human development and concluded that the two main driving forces of human development are the pursuit of profit maximization and the pursuit of ethical perfection and that only by effectively integrating the two can we better promote economic development and social progress and then promote human development [15].…”

Section: Current Status Of Researchmentioning

confidence: 99%

Construction of a Hierarchical Neural Network Power Source Model for Human Capital Technology Innovation and Benefit Distribution with Big Data Analysis

Liu

Tsai

2021

Mathematical Problems in Engineering

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In this paper, a hierarchical neural network power source model is used to conduct an in-depth analysis and research on human capital technology innovation and revenue distribution. A hierarchical neural network analysis method was chosen to evaluate the human capital value of professional degree master students, and the applicability of the index system was confirmed through errors; moreover, the significance of the output results was analyzed according to the weight assignments of the input, implicit, and output layers. The analysis found that there was a large disagreement in the assessment of their human capital value, which led to the lack of practical utility of human capital. Knowledge-skilled talents have a wealth of theoretical knowledge and can use theories to guide related work. Compared with technically skilled high-skilled talents, their educational level is higher, and they can summarize past intuitive experience into theoretical guidance. Therefore, the hierarchical neural network method we constructed is theoretically effective in assessing the value of the human capital of professional master’s students and the role of the main constituents. Based on the assessment results, we can provide policy-informed suggestions for improving the quality of school education. To quickly verify whether the model can converge during the training process, a simple dataset with only two sequences and the elements in the sequences being real numbers rather than vectors are constructed to speed up the computation; meanwhile, the length of the sequences in this dataset is adjustable to initially verify the model’s ability to alleviate the long-time dependence problem.

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“…As one of the most advanced non‐volatile memory technologies, resistive random‐access memory (RRAM) achieves not only a high memristor performance but also programmable reconfigurability, projecting a new trend in the field of IMC. [ 23,24 ] For instance, there are many reports on the RRAM configured IMC structures achieving a high performance in terms of the logic operation [ 25–27 ] with a reduced power consumption of 200 times lesser than those of other traditional devices, [ 28 ] among which, unipolar resistive switching devices have typically been used to realize the key XOR computing by assigning the string information to the voltage applied on two terminals of device. [ 29,30 ] Nevertheless, such operation methods result in an active device distribution in only the diagonal area of a memristor array, consequently causing a severe limitation in the utilization and area efficiencies.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Highly Reliable and Energy Efficient in‐Memory Hamming Distance Computations in 1 Kb 1‐Transistor‐1‐Memristor Arrays

Lin

Liu

et al. 2021

Adv Materials Technologies

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Highly efficient Hamming distance (HD) computations can significantly boost up modern data‐intensive algorithms. However, the traditional complementary metal–oxide–semiconductor devices configured circuits suffer from the huge power consumption with periphery complexity for HD computations. Herein, the implementation of highly reliable and energy efficient in‐memory HD computations in 1 Kb 1‐transistor‐1‐memristor (1T1M) TiN/HfOx/TaOx/TiN array chip is reported. 1T1M devices demonstrate a high on/off ratio of 50, high programming speed of 20 ns, and low energy consumption of 0.224 pJ bit−1. By modulating the 1T1M cell gate and source signal synergistically, the characteristic XOR operations of the binary information are executed in a reliable manner. Importantly, equipped with a stable low resistance state (LRS) distribution (coefficient of variation <11%), the developed 1T1M arrays can implement accurate HD computations between two 8‐bit strings and simultaneously store computing results in the memristors. The complementary studies demonstrate that the stable LRS is attributed to the TaOx built‐in compliance layer which facilitates the transistor surge current reduction during forming and SET, elaborating the significant potential for achieving reliable in‐memory HD computations. Such architecture manifests a 5‐ and 36.89‐fold enhancement of the processing latency and energy efficiency in comparison with latest reports, promoting the fan out of new in‐memory computing applications.

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Hardware design and the competency awareness of a neural network

Cited by 15 publications

References 79 publications

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Construction of a Hierarchical Neural Network Power Source Model for Human Capital Technology Innovation and Benefit Distribution with Big Data Analysis

Implementation of Highly Reliable and Energy Efficient in‐Memory Hamming Distance Computations in 1 Kb 1‐Transistor‐1‐Memristor Arrays

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