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A High-Level Methodology to Evaluate and Optimize Digital Architectures Targeting Spike encoding

IEEE ACCESS 2023年 11卷 120654-120665页

作者： Gillet, Clemence Vincent, Adrien F. Le Gal, Bertrand Saighi, Sylvain Univ Bordeaux CNRS Bordeaux INP IMSUMR 5218 F-33400 Talence France

Spiking Neural Networks (SNNs) are promising candidates for low-power and low-latency embedded artificial intelligence. However, those networks require event-based data produced by neuromorphic sensors which are not widely available, except for a few specialized devices like neuromorphic retinas. For other data types, a solution lies in the use of conventional sensors in conjunction with encoding layers. However, when performed in software, this solution can be detrimental to energy consumption or latency. Here we introduce a flexible design methodology for efficiently implementing, optimizing, and evaluating digital architectures of spike encoding integrating algorithms available in the literature. In order to quickly evaluate different hardware architectures and to tailor the solution to the application needs, our approach relies on High-Level Synthesis (HLS) tools and Python scripting. We illustrate the methodology by generating various digital architectures of two encoding algorithms taken from the literature and we evaluate their energy consumption and timing performances on Field Programmable Gate Arrays. This work could overcome the lack of neuromorphic sensors and accelerate the development of lower-power hardware SNNs.

关键词： Spiking neural networks data encoding high level synthesis spike sorting edge computing

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Low Energy yet Reliable data Communication Scheme for Network-on-Chip

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2015年第12期34卷 1892-1904页

作者： Jafarzadeh, Nima Palesi, Maurizio Eskandari, Saeedeh Hessabi, Shaahin Afzali-Kusha, Ali Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran *** Iran Kore Univ Enna Informat Proc Syst I-94100 Enna Italy Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran 14515775 Iran Sharif Univ Technol Dept Comp Engn Tehran 113659517 Iran Univ Tehran Sch Elect & Comp Engn Coll Engn Tehran 14395 Iran

In this paper, a low energy yet reliable communication scheme for network-on-chip is suggested. To reduce the communication energy consumption, we invoke low-swing signals for transmitting data, as well as data encoding techniques, for minimizing both self and coupling switching capacitance activity factors. To maintain the communication reliability of communication at low-voltage swing, an error control coding (ECC) technique is exploited. The decision about end-to-end or hop-to-hop ECC schemes and the proper number of detectable errors are determined through high-level mathematical analysis on the energy and reliability characteristics of the techniques. Based on the analysis, the extended single error correction double-error detecting end-to-end coding technique with three bits of error detection is used in the network layer. For minimization of the self and coupling switching capacitance activity factors, the odd, even, full invert scheme is employed in the data link layer. This coding has an inherent error detection probability for the flits, which is exploited in the suggested technique. The efficiency of the scheme is studied by using both synthetic and real traffic scenarios. The study reveals savings of up to 43% and 58%, for power dissipation and energy consumption, respectively, without any significant performance degradation and overhead in the network interface.

关键词： data encoding end-to-end coding energy analysis hop-to-hop coding interconnection on chip low power low-swing signaling network-on-chip (NoC) reliable data coding

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On-Chip Communication Energy Reduction Through Reliability Aware Adaptive Voltage Swing Scaling

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2016年第11期35卷 1769-1782页

作者： Mineo, Andrea Palesi, Maurizio Ascia, Giuseppe Pande, Partha Pratim Catania, Vincenzo Univ Catania Dipartimento Ingn Elettr Elettron & Informat I-95125 Catania Italy Kore Univ Enna I-94100 Enna Italy Washington State Univ Pullman WA 99163 USA

In a multi/many-core system, the network-on-chip (NoC)-based communication backbone is responsible for a relevant fraction of the overall energy budget. Reducing the voltage swing for signaling in crossbars and links results in significant energy saving. Unfortunately, as voltage swing reduces, the bit error rate increases, that in turn compromises the communication reliability. Starting from the assumption that not all the communications need same level of reliability, in this paper we propose techniques and architectures for run-time tuning of the voltage swing of the crossbars and interrouter links. The proposed technique is compared with the state of the art in link energy reduction through data encoding under both synthetic and real traffic scenarios. We found that the proposed techniques allow to significantly reduce the energy consumption of the NoC fabric without degrading the performance metrics. Energy savings ranging from 20% to 43% have been observed without any relevant impact on the performance metrics.

关键词： data encoding link power reduction low power network on chip (NoC) power analysis probabilistic CMOS

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Hybrid machine learning detection for orbital angular momentum over turbulent MISO wireless channelInspec keywordsOther keywords

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IET COMMUNICATIONS 2020年第22期14卷 4116-4126页

作者： ElHelaly, Alaa Kafafy, Mai Mehanna, Ahmed H. Khairy, Mohamed M. Cairo Univ Elect & Elect Commun Engn Dept Giza 12613 Egypt

This work proposes a machine learning detection scheme for wireless orbital angular momentum (OAM) communication systems. The new scheme works for single-input-single-output (SISO) and multiple-input-single-output (MISO) wireless communication between the transmitter and the receiver. The transmitter encodes its data in OAM modes which are constructed using Laguerre-Gaussian beam form. The transmitted beams travel through a wireless channel with weak to medium turbulence strength and they arrive at random positions on the same receiver area. The authors proposed detection scheme allows the reception of multiple overlapping beams without prior knowledge of beams centre positions. The proposed scheme uses a novel technique of receiver segmentation and space filtering along with neural network to decode the received beams. Simulations show the detection efficiency and the enhanced performance of their proposed scheme for the SISO case and the MISO case with 2, 4, and 16 beams.

关键词： wireless channels angular momentum learning (artificial intelligence) encoding MISO communication SISO communication radio transceivers neural nets decoding OAM modes Laguerre– Gaussian beam transmitted beams medium turbulence strength multiple overlapping beams receiver segmentation space filtering hybrid machine learning detection turbulent MISO wireless channel wireless orbital angular momentum communication systems OAM communication systems multiple‐ input– single‐ output wireless communication single‐ input– single‐ output wireless communication SISO wireless communication MISO wireless communication data encoding beams centre positions neural network received beam decoding

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AN APPROACH AND ITS SPECIFICATION TO data REFORMATTING IN data CONVERSION

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INFORMATION SYSTEMS 1988年第1期13卷 31-51页

作者： NIEMI, T Department of Computer Science University of Tampere SF-33101 Tampere 10 Finland

Those data conversion cases which require both data restructuring and data reformatting at the same time have been proven to be the most complex and troublesome conversion cases. In this paper we address this issue. Our approach contains those factors in terms of which conversion process can be simplified in complex data conversion cases. We concentrate on data reformatting aspects. data reformatting is a process which changes or reformates the representations of stored data so that they can be processed correctly in a new hardware/software environment. We show that the load and unload utilities of file and database systems provide a meaningful interface between the actual conversion process and the software available in the source and target environments. The complexity of data reformatting can be reduced considerably by utilizing load and unload utilities effectively. In spite of using the load and unload utilities, stored data contain some data base management system (dbms) and file system oriented characteristics which must be reformatted. To reformat these characteristics we define exact encoding functions in terms of which bit string representations of flat files and hierarchical databases can be built up. In this paper we also connect the data reformatting aspects to our previous data restructuring specification for flat files and hierarchical databases. In other words, after this extension we have a precise specification for complex data conversion cases. In this paper we show that the attribute method (attribute grammars) provides a precise and convenient tool for this kind of software specification.

关键词： data reformatting data encoding data conversion data base specification attribute grammars

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Preparation of Quantum Superposition Using Partial Negation

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IEEE ACCESS 2022年 10卷 18944-18954页

作者： Anwer, Sara Younes, Ahmed Elkabani, Islam Elsayed, Ashraf Alexandria Univ Fac Sci Dept Math & Comp Sci Alexandria 21511 Egypt Alexandria Univ Fac Sci Alexandria Quantum Comp Grp Alexandria 21511 Egypt AlAlamein Int Univ Fac Comp Sci & Engn Alalamein 51718 Egypt

The preparation of a quantum superposition is the key to the success of many quantum algorithms and quantum machine learning techniques. The preparation of an incomplete or a non-uniform quantum superposition with certain properties is a non-trivial task. In this paper, an n-qubits variational quantum circuit using partial negation and controlled partial negation operators is proposed to prepare a quantum superposition from a given space of probability distributions. The speed of the preparation process and the accuracy of the prepared superposition has special importance to the success of any quantum algorithm. The proposed method can be used to prepare the required quantum superposition in \mathscr {O}(n) steps and with high accuracy when compared with relevant methods in literature.

关键词： Quantum superposition quantum state partial negation data encoding prepared amplitudes acquired amplitudes

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Recursive Histogram Tracking-Based Rapid Online Anomaly Detection in Cyber-Physical Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第11期52卷 7123-7133页

作者： Kumar, Ratnesh Hossain, Ramij Raja Talukder, Soumyabrata Jena, Amit Al Ghazo, Alaa T. Iowa State Univ Dept Elect & Comp Engn Ames IA 50010 USA Hashemite Univ Dept Mechatron Engn Zarqa 13133 Jordan

Prompt online detection of anomalies induced by malicious attacks enhances the efficacy of real-time operation and mitigation of attack, an indispensable part of any cyber-physical system (CPS) management. This article proposes a novel online rapid detection scheme that continuously monitors the data packet stream and infers the sequence of probability distributions, estimated as histograms, and alerts when a change in the histogram is detected, reporting both the attack as well as an estimate of its instant of commencement. A statistical data-driven attack model is proposed and employed that is general enough to represent two ubiquitous types of attacks on CPS: 1) replay and 2) bias-injection. The proposed detection framework relies on the fact that CPSs possess well-defined dynamics that are affected by quasistationary noise, which allows the histogram sequences of the system data packets to converge (to different distributions under the presence of the attack versus the absence of attack). The proposed online scheme detects an attack, and estimates the attack commencement time by relying on the computed distance between real-time estimated histogram versus apriori learned nominal histogram. Our formulation further sheds light on two different attack initiation-time-based subcases, ``early'' (attack starts before sufficient data of nominal behavior was collected to allow its histogram sequence to be closer to its nominal value) versus ``late.'' The designed algorithm of our scheme has linear time complexities in the dimension of data packets and algorithm parameters, which makes it suited for rapid detection. The proposed algorithm is implemented and validated on two real supervisory control and data acquisition system datasets, where a low detection delay demonstrates the effectiveness of the scheme.

关键词： Histograms Anomaly detection Monitoring Real-time systems Computational modeling Probability distribution data models Anomaly detection bias-injection attack data encoding online detection replay attack

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On-demand assessment of air traffic impact of blocking airspace

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AERONAUTICAL JOURNAL 2018年第1258期122卷 1985-2009页

作者： Srivastava, A. St Clair, T. Pan, G. MITRE Corp Mclean VA 22102 USA

The Federal Aviation Administration often blocks strategically located airspace volumes to ensure safety during a variety of operations that are potentially hazardous to aircraft, such as space launches. As the frequency of these operations increases, there is a growing need to deepen collaboration and transparency between stakeholders regarding the use of airspace. This collaboration can be supported by models and capabilities to quickly assess the impact of airspace closures, up to 12 months into the future. This paper presents a technique to enable a 'what-if' analysis capability coupled with a prediction model, whereby changes in airspace dimension, location, and activation time are reflected instantaneously as measures of projected impact. The technique can also be used for quick post-operations analysis using historical traffic data and to develop air traffic impact assessment capabilities accessible to a broad range of users outside of the air traffic domain. This research has three key components: developing a model to predict air traffic demand up to 12 months into the future, modelling air traffic impact to the affected traffic, and reducing this information into a data structure that can support on-demand analysis. The focus of this paper is on new techniques to predict demand using a large set of historical track data and further encode these projections to support the quick assessment of the impact of blocking various airspace volumes. Initial results show that the proposed data reduction scheme accurately represented the traffic crossing an airspace and resulted in data size reduction by over 50%. The projection model performed well, the actual number of impacted flights were within the estimated range of approximately 80% of the time. Finally, the responsiveness of the web-based prototype developed to illustrate the concept demonstrated the model's ability to support an on-demand assessment of the air traffic impact of blocking airspace. A significant

关键词： NAS impact What-if analysis air traffic projection data reduction data encoding traffic pattern blocked airspaces CDM commercial Space

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Online Fault Diagnosis Using Bioinspired Spike Neural Network

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第9期20卷 11466-11474页

作者： Xu, Lie Ji, Daxiong Zhejiang Univ Inst Marine Elect & Intelligent Syst Ocean Coll Zhoushan 316000 Peoples R China Chinese Acad Sci State Key Lab Robot SIA Shenyang 110016 Peoples R China

data-driven fault diagnosis methods suffer from restrictive assumptions that hinder adaptability to varying work conditions, rendering offline modes insufficient. Addressing this challenge, a bioinspired spike neural network (bio-SNN) is proposed, featuring an innovative online learning mode. The network employs a novel spike encoding method with data compression for efficiently transforming time series data into spike sequences. This encoding method involves converting 1-D series data into a 2-D spectrogram using a filter bank, incorporating a stochastic spike rate for a more flexible representation compared to precise spike rates. The application of a biologically plausible learning rule, specifically spike timing-dependent plasticity (STDP), enhances the adaptability of the network. A horizontal inhibition and homeostasis mechanism are also introduced, facilitating effective online updating of synaptic weights. Experimental results on two well-established fault datasets showcase the advantages of the bio-SNN method over existing approaches, highlighting its potential for robust and adaptive fault diagnosis in practical scenarios.

关键词： Neurons encoding Fault diagnosis Synapses Vibrations Spectrogram Vectors Bioinspired learning data encoding fault diagnosis spike neural network (SNN)

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encoding SEARCH-TREES IN LISTS

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 1982年第3-4期10卷 237-246页

作者： VAISHNAVI, V WOOD, D MCMASTER UNIV COMP SCI UNITHAMILTON L8S 4K1ONTARIOCANADA

We study the optimal encoding of search trees in lists and, in particular, in single-linked lists. We are able to provide an 0(n2) construction algorithm for weighted binary search trees, which can be generalized to g... 详细信息

关键词： data encoding trees search trees lists optimal encodings 3.73 3.74 4.34 5.25

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