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data encoding Schemes in Networks on Chip

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2011年第5期30卷 774-786页

作者： Palesi, Maurizio Ascia, Giuseppe Fazzino, Fabrizio Catania, Vincenzo Kore Univ I-94100 Enna Italy Univ Catania Dipartimento Ingn Informat & Telecomun I-95125 Catania Italy Icera Inc Bristol BS32 4AQ Avon England

An ever more significant fraction of the overall power dissipation of a network-on-chip (NoC) based system-on-chip (SoC) is due to the interconnection system. In fact, as technology shrinks, the power contribute of NoC links starts to compete with that of NoC routers. In this paper, we propose the use of data encoding techniques as a viable way to reduce both power dissipation and energy consumption of NoC links. The proposed encoding scheme exploits the wormhole switching techniques and works on an end-to-end basis. That is, flits are encoded by the network interface (NI) before they are injected in the network and are decoded by the destination NI. This makes the scheme transparent to the underlying network since the encoder and decoder logic is integrated in the NI and no modification of the routers architecture is required. We assess the proposed encoding scheme on a set of representative data streams (both synthetic and extracted from real applications) showing that it is possible to reduce the power contribution of both the self-switching activity and the coupling switching activity in inter-routers links. As results, we obtain a reduction in total power dissipation and energy consumption up to 37% and 18%, respectively, without any significant degradation in terms of both performance and silicon area.

关键词： Coupling capacitance data encoding low power network on chip (NoC) power analysis

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data encoding techniques to improve the performance of System on Chip

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第2期34卷 492-503页

作者： Chennakesavulu, M. Prasad, T. Jayachandra Sumalatha, V JNTUA Dept Elect & Commun Engn Ananthapuramu Andhra Pradesh India RGMCET Nandyal 515001 Andhra Pradesh India RGMCET Dept Elect & Commun Engn Nandyal 518501 Andhra Pradesh India JNTUA Coll Engn Dept Elect & Commun Engn Ananthapuramu 515001 Andhra Pradesh India H 26-192-C-2 Nandyal 518501 Andhra Pradesh India

The concept of System on Chip (SoC) has introduced many opportunities but also many challenges and hurdles in Ultra Large Scale Integration (ULSI). In fact the global interconnects are undergoing a reverse scaling process in SoC which has resulted in a wider, thicker top metal layers and an increase in the wire aspect ratio. These impacts have increased the self and coupling capacitance of interconnects, leading to on-chip data communication to become more power consuming and less reliable. In this paper, an attempt has been made to propose two data encoding techniques namely 1) Odd-Even-Full-Normal inversion considering the total self and coupling switching activity (OEFNSC) 2) OEFNSC with segmentation (OEFNSC-SEG) to reduce the switching activity across self and coupling capacitance. These data encoding techniques have lead to the reduction of dynamic power and improve the reliability. The results proved the effectiveness of the proposed data-encoding schemes, in terms of Energy, Delay and Energy Delay-Product efficiency of 8-bit, 16-bit, 32-bit and 64-bit respectively in 45 nm technology and these efficiencies have further improved by adopting the segmentation of proposed data encoding scheme. (c) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Switching activity Crosstalk classes data encoding Low power System on Chip (SoC)

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data encoding Techniques for Reducing Energy Consumption in Network-on-Chip

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2014年第3期22卷 675-685页

作者： Jafarzadeh, Nima Palesi, Maurizio Khademzadeh, Ahmad Afzali-Kusha, Ali Islamic Azad Univ Sci & Res Branch Dept Comp Sci Tehran 14515775 Iran Kore Univ Enna I-94100 Enna Italy Iran Telecommun Res Ctr Tehran *** Iran Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran 14395 Iran

As technology shrinks, the power dissipated by the links of a network-on-chip (NoC) starts to compete with the power dissipated by the other elements of the communication subsystem, namely, the routers and the network interfaces (NIs). In this paper, we present a set of data encoding schemes aimed at reducing the power dissipated by the links of an NoC. The proposed schemes are general and transparent with respect to the underlying NoC fabric (i.e., their application does not require any modification of the routers and link architecture). Experiments carried out on both synthetic and real traffic scenarios show the effectiveness of the proposed schemes, which allow to save up to 51% of power dissipation and 14% of energy consumption without any significant performance degradation and with less than 15% area overhead in the NI.

关键词： Coupling switching activity data encoding interconnection on chip low power network-on-chip (NoC) power analysis

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data encoding with Generative AI: Towards Improved Machine Learning Performance

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第10期15卷 53-57页

作者： Saouabe, Abdelkrim Oualla, Hicham Mourtaji, Imad AKKODIS Res Paris France

This article explores the design and implementation of a Generative AI-based data encoding system aimed at enhancing human resource management processes. Addressing the complexity of HR data and the need for informed decision-making, the study introduces a novel approach that leverages Generative AI for data encoding. This approach is applied to an HR database to develop a machine learning model designed to create a salary simulator, capable of generating accurate and personalized salary estimates based on factors such as work experience, skills, geographical location, and market trends. The aim of this approach is to improve the performance of the machine learning model. Experimental results indicate that this encoding approach improves the accuracy and fairness of salary determinations. Overall, the article demonstrates how AI can revolutionize HR management by delivering innovative solutions for more equitable and strategic compensation practices.

关键词： data encoding Generative AI salary simulator human resource management machine learning

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data encoding for Low-Power in Wormhole-Switched Networks-on-Chip

Data Encoding for Low-Power in Wormhole-Switched Networks-on...

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12th Euromicro Conference on Digital System Design, Architectures, Methods and Tools

作者： Palesi, Maurizio Fazzino, Fabrizio Ascia, Giuseppe Catania, Vincenzo Univ Catania DIIT I-95124 Catania Italy

ISBN: (纸本)9780769537825

As the number of cores in a chip increases, the role played by the communication system becomes more and more central. An on-chip communication infrastructure based on the Network-on-Chip (NoC) paradigm is today recognized as the most effective and scalable solution able to deal with the communication issues that will characterize the next generation of many-cores architectures. An ever more significant fraction of the overall chip area is devoted to support advanced and reliable communication protocols making the energy resources used for communication starting to compete with the ones spent for computation. Amongst the communication resources, as technology shrinks, the power ratio between NoC links and routers increases making the links becoming more power-hungry than routers. In this paper we propose a novel end-to-end data encoding scheme which exploits the wormhole technique commonly used in NoC-based system to reduce power dissipated by the NoC links. We assess the proposed encoding scheme on a set of representative data streams showing that it is possible to reduce the power contribution of both the self switching activity and the coupling switching activity in inter-routers links. As results, we obtain a reduction in total power dissipation and energy consumption up to 26% and 9% respectively without any significant degradation in terms of both performance and silicon area. The encoder and decoder logic is integrated in the network interface and is transparent to the underling NoC.

关键词： Network on Chip Low power data encoding Coupling capacitance Power analysis

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Relieving Write Disturbance for Phase Change Memory With RESET-Aware data encoding

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IEEE TRANSACTIONS ON COMPUTERS 2024年第8期73卷 1939-1952页

作者： Wu, Ronglong Shen, Zhirong Chen, Jianqiang Zheng, Chengshuo Yang, Zhiwei Shu, Jiwu Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Innovat Lab Sci & Technol Energy Mat Fujian Prov I Xiamen 361102 Peoples R China Minjiang Univ Fuzhou 350108 Peoples R China

The write disturbance (WD) problem is becoming increasingly severe in PCM due to the continuous scaling down of memory technology. Previous studies have attempted to transform WD-vulnerable data patterns of the new data to alleviate the WD problem. However, through a wide spectrum of real-world benchmarks, we have discovered that simply transforming WD-vulnerable data patterns does not proportionally reduce (or may even increase) WD errors. To address this issue, we present ResEnc, a RESET-aware data encoding scheme that reduces RESET operations to mitigate the WD problem in both wordlines and bitlines for PCM. It dynamically establishes a mask word for each block for data encoding and adaptively selects an appropriate encoding granularity based on the diverse write patterns. ResEnc finally reassigns the mask words of unchanged blocks to changed blocks for exploring a further reduction of WD errors. Extensive experiments show that ResEnc can reduce 16.8-87.0% of WD errors, shorten 5.6-39.6% of write latency, and save 7.0-43.1% of write energy for PCM.

关键词： Phase change materials encoding Nonvolatile memory Heating systems Codes Transforms Error correction codes Phase change memory write disturbance data encoding

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Zero Aware Configurable data encoding by Skipping Transfer for Error Resilient Applications

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2021年第8期68卷 3337-3350页

作者： Jha, Chandan Kumar Singh, Shreyas Thakker, Riddhi Awasthi, Manu Mekie, Joycee Indian Inst Technol Gandhinagar Discipline Elect Engn Gandhinagar 382355 Gandhinagar India IIT Dept Elect Engn Mumbai 400076 Maharashtra India Indian Inst Technol Gandhinagar Discipline Comp Sci & Engn Gandhinagar 382355 Gandhinagar India Indian Inst Technol Gandhinagar Disciple Elect Engn Gandhinagar 382355 Gandhinagar India Dhirubhai Ambani Inst Informat & Commun Technol Discipline Informat & Commun Technol Gandhinagar 382007 India Oracle India Gurgaon 382355 India Ashoka Univ Dept Comp Sci Sonipat 131029 India

data transfer across DRAM channels accounts for nearly a quarter of the total energy consumption of DDR4 DRAMs. Modern applications with high bandwidth requirements further increase channel energy consumption. However, channel energy consumption is dependent on data being transferred. Pseudo Open Drain (POD) asymmetric termination, used in current DDR4 systems, consumes energy only when 1's are being transmitted over the channels. Many modern applications, including AI/ML ones are resilient to errors in data, and can work well with approximate data. This resilience can vary widely across and within applications, which provides a number of ways for exploiting these characteristics to save data transfer energy across the DRAM channel. However, all DRAM data encoding schemes have been targeted towards applications that require exact data and are not approximation resilient. In this paper, we propose Zero Aware Configurable data encoding by Skipping Transfer (ZAC-DEST), a data encoding scheme to reduce the energy consumption of DRAM channels, specifically targeted towards approximate computing and error resilient applications. ZAC-DEST exploits the similarity between recent data transfers across channels and information about error resilience behaviour of applications to reduce on-die termination and switching energy by reducing the number of 1's transmitted over the channels. ZAC-DEST also provides a number of knobs for trading off application's accuracy for energy savings, and vice versa, and can be applied to both training and inference. We apply ZAC-DEST to five machine learning applications. On average, across all applications and configurations, we observed a reduction of 40% in termination energy and 37% in switching energy as compared to the state of the art data encoding technique BD-Coder with an average output quality loss of 10%. We show that if both training and testing are done assuming the presence of ZAC-DEST, the output quality of the applications can b

关键词： data encoding DRAM channels approximate computing machine learning

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Power analysis attack resilient block cipher implementation based on 1-of-4 data encoding

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ETRI JOURNAL 2021年第4期43卷 746-757页

作者： Shanmugham, Shanthi Rekha Paramasivam, Saravanan PSG Coll Technol Dept ECE Coimbatore Tamil Nadu India

Side-channel attacks pose an inevitable challenge to the implementation of cryptographic algorithms, and it is important to mitigate them. This work identifies a novel data encoding technique based on 1-of-4 codes to resist differential power analysis attacks, which is the most investigated category of side-channel attacks. The four code words of the 1-of-4 codes, namely (0001, 0010, 1000, and 0100), are split into two sets: set-0 and set-1. Using a select signal, the data processed in hardware is switched between the two encoding sets alternately such that the Hamming weight and Hamming distance are equalized. As a case study, the proposed technique is validated for the NIST standard AES-128 cipher. The proposed technique resists differential power analysis performed using statistical methods, namely correlation, mutual information, difference of means, and Welch's t-test based on the Hamming weight and distance models. The experimental results show that the proposed countermeasure has an area overhead of 2.3x with no performance degradation comparatively.

关键词： data encoding differential power analysis hamming weight distance equalization hiding technique register transfer level countermeasure

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Privacy-Preserving Probabilistic data encoding for IoT data Analysis

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 9173-9187页

作者： Zaman, Zakia Xue, Wanli Gauravaram, Praveen Hu, Wen Jiang, Jiaojiao Jha, Sanjay K. Univ New South Wales UNSW Inst Cybersecur IFCYBER Sydney NSW 2052 Australia Cyber Secur Cooperat Res Ctr Joondalup WA 6027 Australia Tata Consultancy Serv Ltd Brisbane Qld 2060 Australia

The widespread integration of the Internet of Things (IoT) is crucial in advancing sustainable development. IoT service providers actively collect user data for analysis using sophisticated Deep Learning (DL) algorithms. This enables the extraction of valuable insights for business intelligence and improving service quality. However, as these datasets contain sensitive personal information, there is a risk of privacy breaches when DL models are employed. This vulnerability may result in Membership Inference Attacks (MIA), potentially leading to the unauthorized disclosure of highly sensitive data. Therefore, developing an efficient and privacy-preserving data analysis system for IoT is imperative. Recent research has highlighted the effectiveness of utilizing Bloom Filter (BF)-encoding in conjunction with Differential Privacy (DP) for safeguarding privacy during data analysis. Given its attributes of low complexity and high utility, this approach proves effective, particularly in resource-constrained IoT domains. With this in mind, we propose a novel framework for privacy-preserving IoT data analysis based on BF-encoded data. Our research introduces an innovative BF-encoding technique combined with Local Differential Privacy (LDP), capable of efficiently encoding various types of IoT data (such as facial images and smart-meter data) while maintaining privacy when integrated into DL algorithms for downstream analysis. Experimental results demonstrate that our BF-encoded data surpasses the utility of standard BF-encoded data when utilized in DL algorithms for downstream tasks, showcasing an approximate 30% improvement in classification accuracy. Furthermore, we assess the privacy of these DL models against MIA, revealing that attackers can only make random guesses with an accuracy of approximately 50%.

关键词： data privacy encoding Differential privacy data models Privacy data analysis Cloud computing data encoding bloom filter privacy-preserving machine learning differential privacy privacy utility

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Time series data encoding in Apache IoTDB: comparative analysis and recommendation

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VLDB JOURNAL 2024年第3期33卷 727-752页

作者： Xia, Tianrui Xiao, Jinzhao Huang, Yuxiang Hu, Changyu Song, Shaoxu Huang, Xiangdong Wang, Jianmin Tsinghua Univ Beijing Peoples R China

Not only the vast applications but also the distinct features of time series data stimulate the booming growth of time series database management systems, such as Apache IoTDB, InfluxDB, OpenTSDB and so on. Almost all these systems employ columnar storage, with effective encoding of time series data. Given the distinct features of various time series data, different encoding strategies may perform variously. In this study, we first summarize the features of time series data that may affect encoding performance. We also introduce the latest feature extraction results in these features. Then, we introduce the storage scheme of a typical time series database, Apache IoTDB, prescribing the limits to implementing encoding algorithms in the system. A qualitative analysis of encoding effectiveness is then presented for the studied algorithms. To this end, we develop a benchmark for evaluating encoding algorithms, including a data generator and several real-world datasets. Also, we present an extensive experimental evaluation. Remarkably, a quantitative analysis of encoding effectiveness regarding to data features is conducted in Apache IoTDB. Finally, we recommend the best encoding algorithm for different time series referring to their data features. Machine learning models are trained for the recommendation and evaluated over real-world datasets.

关键词： Time series data encoding data compression

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