IEEE Nanotechnology Magazine最新文献_第9页

Memristor-Based Binarized Spiking Neural Networks: Challenges and applications 基于忆阻器的二值化脉冲神经网络:挑战与应用

IF 1.6

IEEE Nanotechnology Magazine Pub Date : 2022-04-01 DOI: 10.1109/mnano.2022.3141443

J. Eshraghian, Xinxin Wang, W. Lu

{"title":"Memristor-Based Binarized Spiking Neural Networks: Challenges and applications","authors":"J. Eshraghian, Xinxin Wang, W. Lu","doi":"10.1109/mnano.2022.3141443","DOIUrl":"https://doi.org/10.1109/mnano.2022.3141443","url":null,"abstract":"Memristive arrays are a natural fit to implement spiking neural network (SNN) acceleration. Representing information as digital spiking events can improve noise margins and tolerance to device variability compared to analog bitline current summation approaches to multiply–accumulate (MAC) operations. Restricting neuron activations to single-bit spikes also alleviates the significant analog-to-digital converter (ADC) overhead that mixed-signal approaches have struggled to overcome. Binarized, and more generally, limited-precision, NNs are considered to trade off computational overhead with model accuracy, but unlike conventional deep learning models, SNNs do not encode information in the precision-constrained amplitude of the spike. Rather, information may be encoded in the spike time as a temporal code, in the spike frequency as a rate code, and in any number of stand-alone and combined codes. Even if activations and weights are bounded in precision, time can be thought of as continuous and provides an alternative dimension to encode information in. This article explores the challenges that face the memristor-based acceleration of NNs and how binarized SNNs (BSNNs) may offer a good fit for these emerging hardware systems.","PeriodicalId":44724,"journal":{"name":"IEEE Nanotechnology Magazine","volume":"16 1","pages":"14-23"},"PeriodicalIF":1.6,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43832864","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

SpinQ Triangulum: A commercial three-qubit desktop quantum computer SpinQ Triangulum:商用三量子位桌面量子计算机

IF 1.6

IEEE Nanotechnology Magazine Pub Date : 2022-02-07 DOI: 10.1109/mnano.2022.3175392

Guanru Feng, Shin-Yao Hou, Hongyang Zhou, Wei Shi, Shengqiang Yu, Zikai Sheng, Xin Rao, Kaihong Ma, Chenxing Chen, Bing Ren, Guozing Miao, Jingen Xiang, B. Zeng

引用次数: 2

Greetings From IEEE NTC President Fabrizio Lombardi [President’s Message] IEEE NTC主席Fabrizio Lombardi的问候[主席致辞]

IF 1.6

IEEE Nanotechnology Magazine Pub Date : 2022-02-01 DOI: 10.1109/mnano.2021.3126032

Fabrizio Lombardi

引用次数: 0

Integrated Nanotechnology 2.0: 3D, Smart, Flexible, and Dynamic [Highlights] 集成纳米技术2.0:3D、智能、灵活和动态

IF 1.6

IEEE Nanotechnology Magazine Pub Date : 2022-02-01 DOI: 10.1109/MNANO.2021.3126129

D. Gracias

引用次数: 1

Approximate Computing [Guest Editorial] 近似计算[客座评论]

IF 1.6

IEEE Nanotechnology Magazine Pub Date : 2022-02-01 DOI: 10.1109/mnano.2021.3126030

Jie Han, Weiqiang Liu

引用次数: 0

CMOS Invertible Logic: Bidirectional operation based on the probabilistic device model and stochastic computing CMOS可逆逻辑:基于概率器件模型和随机计算的双向运算

IF 1.6

IEEE Nanotechnology Magazine Pub Date : 2022-02-01 DOI: 10.1109/mnano.2021.3126094

N. Onizawa, T. Hanyu

引用次数: 3

Always-On Speech Recognition Terminals: Designs based on approximate computing methods 基于近似计算方法的永在线语音识别终端设计

IF 1.6

IEEE Nanotechnology Magazine Pub Date : 2022-02-01 DOI: 10.1109/mnano.2021.3126096

Qin Li, Zheyu Liu, Xinghua Yang, Fei Qiao

引用次数: 0

Approximate Computing [The Editors’ Desk] 近似计算[编辑台]

IF 1.6

IEEE Nanotechnology Magazine Pub Date : 2022-02-01 DOI: 10.1109/mnano.2021.3126031

Bing J. Sheu, Xiaoning Jiang

引用次数: 0

Probabilistic Approximate Computing at Nanoscales: From data structures to memories 纳米尺度的概率近似计算：从数据结构到存储器

IF 1.6

IEEE Nanotechnology Magazine Pub Date : 2022-02-01 DOI: 10.1109/mnano.2021.3126092

Shanshan Liu, P. Reviriego, P. Junsangsri, Fabrizio Lombardi

{"title":"Probabilistic Approximate Computing at Nanoscales: From data structures to memories","authors":"Shanshan Liu, P. Reviriego, P. Junsangsri, Fabrizio Lombardi","doi":"10.1109/mnano.2021.3126092","DOIUrl":"https://doi.org/10.1109/mnano.2021.3126092","url":null,"abstract":"The slowdown of CMOS technology scaling has placed architectures and algorithms on focus for future performance improvements in nanoscale computing systems. Two promising approaches at algorithmic level are approximate computing (AC) and probabilistic data structures (PDSs) that employ the tolerance of an application to small deviations in the results for reducing the complexity of the hardware implementation. AC focuses on applications that process numerical data and relies mostly on approximate (or inexact) low-level arithmetic operations. Instead, PDSs target categorical data and rely on shared data structures and other higher-level simplifications that introduce probabilistic deviations even when all operations are exact. Both AC and PDSs have been able to dramatically reduce the cost in some applications, but they are so far completely disconnected in the application domains, the abstraction levels, and the research communities. In this article, we introduce probabilistic approximate computing (PAC), a new paradigm to use application tolerance for small deviations to reduce the implementation complexity of data structures and hardware when implemented with nanoscale memory technologies. Its goal is to have data structures on which both AC and probabilistic techniques are used in a synergetic way to improve efficiency, while keeping deviations within acceptable margins.","PeriodicalId":44724,"journal":{"name":"IEEE Nanotechnology Magazine","volume":"16 1","pages":"16-24"},"PeriodicalIF":1.6,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45226247","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: 1

Magnetic Random-Access Memory-Based Approximate Computting: An overview 基于磁随机存取存储器的近似计算:综述

IF 1.6

IEEE Nanotechnology Magazine Pub Date : 2022-02-01 DOI: 10.1109/mnano.2021.3126093

You Wang, Kaili Zhang, Bo Wu, Deming Zhang, Hao Cai, Weisheng Zhao

引用次数: 1