Neuromorph. Comput. Eng.最新文献

Editorial: Focus on Neuromorphic Circuits and Systems using Emerging Devices 社论：聚焦使用新兴设备的神经形态电路和系统

Neuromorph. Comput. Eng. Pub Date : 2024-01-30 DOI: 10.1088/2634-4386/ad1cd8

C. S. Thakur, Udayan Ganguly

引用次数: 0

Reducing reservoir computer hyperparameter dependence by external timescale tailoring 通过外部时间尺度裁剪降低水库计算机超参数依赖性

Neuromorph. Comput. Eng. Pub Date : 2024-01-10 DOI: 10.1088/2634-4386/ad1d32

L. Jaurigue, Kathy Lüdge

引用次数: 2

Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications 人类活动识别:边缘AIoT应用的神经形态方法的适用性

Neuromorph. Comput. Eng. Pub Date : 2022-01-17 DOI: 10.1088/2634-4386/ac4c38

V. Fra, Evelina Forno, Riccardo Pignari, T. Stewart, E. Macii, Gianvito Urgese

{"title":"Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications","authors":"V. Fra, Evelina Forno, Riccardo Pignari, T. Stewart, E. Macii, Gianvito Urgese","doi":"10.1088/2634-4386/ac4c38","DOIUrl":"https://doi.org/10.1088/2634-4386/ac4c38","url":null,"abstract":"\u0000 Human activity recognition (HAR) is a classification problem involving time-dependent signals produced by body monitoring, and its application domain covers all the aspects of human life, from healthcare to sport, from safety to smart environments. As such, it is naturally well suited for on-edge deployment of personalized point-of-care (POC) analyses or other tailored services for the user. However, typical smart and wearable devices suffer from relevant limitations regarding energy consumption, and this significantly hinders the possibility for successful employment of edge computing for tasks like HAR. In this paper, we investigate how this problem can be mitigated by adopting a neuromorphic approach. By comparing optimized classifiers based on traditional deep neural network (DNN) architectures as well as on recent alternatives like the Legendre Memory Unit (LMU), we show how spiking neural networks (SNNs) can effectively deal with the temporal signals typical of HAR providing high performances at a low energy cost. By carrying out an application-oriented hyperparameter optimization, we also propose a methodology flexible to be extended to different domains, to enlarge the field of neuro-inspired classifier suitable for on-edge artificial intelligence of things (AIoT) applications.","PeriodicalId":114772,"journal":{"name":"Neuromorph. Comput. Eng.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125623268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Evolutionary 2D organic crystals for optoelectronic transistors and neuromorphic computing 用于光电晶体管和神经形态计算的进化二维有机晶体

Neuromorph. Comput. Eng. Pub Date : 2022-01-12 DOI: 10.1088/2634-4386/ac4a84

Fangsheng Qian, Xiaobo Bu, Junjie Wang, Ziyu Lv, Su‐Ting Han, Ye Zhou

{"title":"Evolutionary 2D organic crystals for optoelectronic transistors and neuromorphic computing","authors":"Fangsheng Qian, Xiaobo Bu, Junjie Wang, Ziyu Lv, Su‐Ting Han, Ye Zhou","doi":"10.1088/2634-4386/ac4a84","DOIUrl":"https://doi.org/10.1088/2634-4386/ac4a84","url":null,"abstract":"\u0000 Brain-inspired neuromorphic computing has been extensively researched, taking advantage of increased computer power, the acquisition of massive data, and algorithm optimization. Neuromorphic computing requires mimicking synaptic plasticity and enables near-in-sensor computing. In synaptic transistors, how to elaborate and examine the link between microstructure and characteristics is a major difficulty. Due to the absence of interlayer shielding effects, defect-free interfaces, and wide spectrum responses, reducing the thickness of organic crystals to the 2D limit has a lot of application possibilities in this computing paradigm. This paper presents an update on the progress of 2D organic crystal-based transistors for data storage and neuromorphic computing. The promises and synthesis methodologies of 2D organic crystals are summarized. Following that, applications of 2D organic crystals for ferroelectric nonvolatile memory, circuit-type optoelectronic synapses, and neuromorphic computing are addressed. Finally, new insights and challenges for the field's future prospects are presented, pushing the boundaries of neuromorphic computing even farther.","PeriodicalId":114772,"journal":{"name":"Neuromorph. Comput. Eng.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114938520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Ferroelectric-based synapses and neurons for neuromorphic computing 基于铁电的突触和神经元用于神经形态计算

Neuromorph. Comput. Eng. Pub Date : 2022-01-07 DOI: 10.1088/2634-4386/ac4918

E. Covi, H. Mulaosmanovic, B. Max, S. Slesazeck, T. Mikolajick

{"title":"Ferroelectric-based synapses and neurons for neuromorphic computing","authors":"E. Covi, H. Mulaosmanovic, B. Max, S. Slesazeck, T. Mikolajick","doi":"10.1088/2634-4386/ac4918","DOIUrl":"https://doi.org/10.1088/2634-4386/ac4918","url":null,"abstract":"\u0000 The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as Spiking Neural Networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems' power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technology for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.","PeriodicalId":114772,"journal":{"name":"Neuromorph. Comput. Eng.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128599423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

Evolutionary vs imitation learning for neuromorphic control at the edge 边缘神经形态控制的进化与模仿学习

Neuromorph. Comput. Eng. Pub Date : 2021-12-22 DOI: 10.1088/2634-4386/ac45e7

Catherine D. Schuman, R. Patton, Shruti R. Kulkarni, Maryam Parsa, Christopher G. Stahl, N. Haas, J. P. Mitchell, Shay Snyder, Amelie Nagle, Alexandra Shanafield, T. Potok

引用次数: 9

Performance of reservoir computing in a random network of single-walled carbon nanotubes complexed with polyoxometalate 多金属氧酸单壁碳纳米管随机网络储层计算性能

Neuromorph. Comput. Eng. Pub Date : 2021-12-15 DOI: 10.1088/2634-4386/ac4339

M. Akai‐Kasaya, Yuki Takeshima, Shaohua Kan, K. Nakajima, T. Oya, T. Asai

引用次数: 18

Two-dimensional molybdenum disulfide artificial synapse with high sensitivity 高灵敏度二维二硫化钼人工突触

Neuromorph. Comput. Eng. Pub Date : 2021-12-15 DOI: 10.1088/2634-4386/ac4338

Hao Huang, Lu Liu, Cheng-Wei Jiang, Jiangdong Gong, Yao Ni, Zhipeng Xu, Huanhuan Wei, Haiyang Yu, Wentao Xu

引用次数: 3

Introducing "Neuromorphic Computing and Engineering" 介绍“神经形态计算与工程”

Neuromorph. Comput. Eng. Pub Date : 2021-05-30 DOI: 10.1088/2634-4386/AC0A5B

G. Indiveri