2021 International Conference on Rebooting Computing (ICRC)最新文献_第2页

A Quantum Method for Subchannel allocation in Device-to-Device Communication 设备对设备通信中子信道分配的量子方法

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00017

M. Saravanan, R. Sircar

{"title":"A Quantum Method for Subchannel allocation in Device-to-Device Communication","authors":"M. Saravanan, R. Sircar","doi":"10.1109/ICRC53822.2021.00017","DOIUrl":"https://doi.org/10.1109/ICRC53822.2021.00017","url":null,"abstract":"Spectrum is an expensive commodity in any wireless communication problem and proper utilization of spectrum is always the goal. In Device-to-Device (D2D) communications, the users are allocated a spectrum that is already pre-allocated to them. However, the challenge of the scheme is that it causes mutual interference between D2D and nearby cellular communication. Thus, there is a need to reduce the mutual interference. To do so, resource allocation, such as sub channel and power allocation, is effective. To manage the resource and achieve the maximum total system capacity, one needs to solve a non-deterministic polynomial (NP) hard optimization problem. This is typically done using heuristics. The choice of any heuristic should look for faster optimization of multiple variables that not only consider uplink and downlink interference but also inter-channel interference between n-devices. To efficiently solve the problem, we have used capacitated Max k-Cut formulation and run in a metaheuristic system like Quantum annealer. We believe and proved that QUBO is a better-suited approach to the D2D resource subchannel allocation problem in this paper.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126527653","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: 0

Proceedings 2021 International Conference on Rebooting Computing ICRC 2021 2021年重启计算机国际会议论文集

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/icrc53822.2021.00002

引用次数: 0

[Copyright notice] (版权)

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/icrc53822.2021.00003

引用次数: 0

Conscious Machines for Autonomous Agents and Cybersecurity 自主代理和网络安全的意识机器

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.36227/techrxiv.17308406

A. Kadin

{"title":"Conscious Machines for Autonomous Agents and Cybersecurity","authors":"A. Kadin","doi":"10.36227/techrxiv.17308406","DOIUrl":"https://doi.org/10.36227/techrxiv.17308406","url":null,"abstract":"Although consciousness has been difficult to define, most researchers in artificial intelligence would agree that AI systems to date have not exhibited anything resembling consciousness. But is a conscious machine possible in the near future? I suggest that a new definition of consciousness may provide a basis for developing a conscious machine. The key is pattern recognition of correlated events in time, leading to the identification of a unified self-agent. Such a conscious system can create a simplified virtual environment, revise it to reflect updated sensor inputs, and partition the environment into self, other agents, and relevant objects. It can track recent time sequences of events, predict future events based on models and patterns in memory, and attribute causality to events and agents. It can make rapid decisions based on incomplete data, and can dynamically learn new responses based on appropriate measures of success and failure. The central aspect of consciousness is the generation of a dynamic narrative, a real-time model of a self-agent pursuing goals in a virtual reality. A conscious machine of this type may be implemented using an appropriate neural network linked to episodic memories. Near-term applications may include autonomous vehicles and online agents for cybersecurity.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121609829","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: 0

Hierarchical Network Partitioning for Reconfigurable Large-Scale Neuromorphic Systems 可重构大规模神经形态系统的分层网络划分

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00020

Nishant Mysore, Gopabandhu Hota, S. Deiss, B. Pedroni, G. Cauwenberghs

引用次数: 2

A Case for Noisy Shallow Gate-based Circuits in Quantum Machine Learning 量子机器学习中基于噪声浅门电路的研究

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00015

Patrick Selig, Niall Murphy, Ashwin Sundareswaran R, D. Redmond, Simon Caton

{"title":"A Case for Noisy Shallow Gate-based Circuits in Quantum Machine Learning","authors":"Patrick Selig, Niall Murphy, Ashwin Sundareswaran R, D. Redmond, Simon Caton","doi":"10.1109/ICRC53822.2021.00015","DOIUrl":"https://doi.org/10.1109/ICRC53822.2021.00015","url":null,"abstract":"There is increasing interest in the development of gate- based quantum circuits for the training of machine learning models. Yet, little is understood concerning the parameters of circuit design, and the effects of noise and other measurement errors on the performance of quantum machine learning models. In this paper, we explore the practical implications of key circuit design parameters (number of qubits, depth etc.) using several standard machine learning datasets and IBM's Qiskit simulator. In total we evaluate over 6500 unique circuits with n ≈ 120700 individual runs. We find that in general shallow (low depth) wide (more qubits) circuit topologies tend to outperform deeper ones in settings without noise. We also explore the implications and effects of different notions of noise and discuss circuit topologies that are more / less robust to noise for classification machine learning tasks. Based on the findings we define guidelines for circuit topologies that show near-term promise for the realisation of quantum machine learning algorithms using gate-based NISQ quantum computer.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133555999","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: 2

Reviewers ICRC 2021 红十字国际委员会2021

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/icrc53822.2021.00010

引用次数: 0

A Reconfigurable Field-Coupled Nanocomputing Paradigm on Uniform Molecular Monolayers 均匀分子单层上的可重构场耦合纳米计算范式

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00028

Yuri Ardesi, G. Beretta, Christian Fabiano, M. Graziano, G. Piccinini

{"title":"A Reconfigurable Field-Coupled Nanocomputing Paradigm on Uniform Molecular Monolayers","authors":"Yuri Ardesi, G. Beretta, Christian Fabiano, M. Graziano, G. Piccinini","doi":"10.1109/ICRC53822.2021.00028","DOIUrl":"https://doi.org/10.1109/ICRC53822.2021.00028","url":null,"abstract":"The Molecular Field-Coupled Nanocomputing (FCN) is a computing beyond-CMOS paradigm that encodes the information in the charge distribution of molecules and propagates it through local electrostatic coupling. Notwithstanding the incredibly high potentialities of this technology in the field of high-speed and low-power digital electronics, a molecular prototype has not been produced yet. Indeed, this technology requires nanometric layouts, which are challenging to obtain, slowing down the technology assessment. In this work, we propose a paradigm that bypasses the need for nanometric patterning of molecular devices by organizing the uniform Self-Assembled Monolayer (SAM) into molecular blocks that may store information and be activated independently. The activation of blocks configures the SAM to perform in-memory logic computation. This study demonstrates a reconfigurable molecular standard-cell that maps the basic logic gates (routing, majority voters, inverters), enabling complex digital circuit design. With this paradigm, we move the challenges from the SAM nanopatterning to the clocking system technological feasibility, reducing resolution constraints and favoring the eventual realization of a prototype.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114361956","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: 2

Spiking Neural Streaming Binary Arithmetic 脉冲神经流二进制算法

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.48550/arXiv.2203.12662

J. Aimone, A. Hill, William M. Severa, C. Vineyard

引用次数: 4

Exploring Spiking Neural Networks in Single and Multi-agent RL Methods 探索单智能体和多智能体强化学习方法中的峰值神经网络

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00023

M. Saravanan, P. S. Kumar, Kaushik Dey, Sreeja Gaddamidi, Adhesh Reghu Kumar

{"title":"Exploring Spiking Neural Networks in Single and Multi-agent RL Methods","authors":"M. Saravanan, P. S. Kumar, Kaushik Dey, Sreeja Gaddamidi, Adhesh Reghu Kumar","doi":"10.1109/ICRC53822.2021.00023","DOIUrl":"https://doi.org/10.1109/ICRC53822.2021.00023","url":null,"abstract":"Reinforcement Learning (RL) techniques can be used effectively to solve a class of optimization problems that require the trajectory of the solution rather than a single-point solution. In deep RL, traditional neural networks are used to model the agent's value function which can be used to obtain the optimal policy. However, traditional neural networks require more data and will take more time to train the network, especially in offline policy training. This paper investigates the effectiveness of implementing deep RL with spiking neural networks (SNNs) in single and multi-agent environments. The advantage of using SNNs is that we require fewer data to obtain good policy and also it is less time-consuming than the traditional neural networks. An important criterion to check for while using SNNs is proper hyperparameter tuning which controls the rate of convergence of SNNs. In this paper, we control the hyperparameter time-step (dt) which affects the spike train generation process in the SNN model. Results on both single-agent and multi-agent environments show that these SNN based models under different time-step (dt) require a lesser number of episodes training to achieve the higher average reward.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131288337","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