A Framework for Collaborative Learning in Secure High-Dimensional Space

Imani, Mohsen; Kim, Yeseong; Riazi, Sadegh; Messerly, John; Liu, Patric; Koushanfar, Farinaz; Rosing, Tajana

doi:10.1109/cloud.2019.00076

Cited by 82 publications

(38 citation statements)

References 40 publications

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“…Thus, while this work could be considered as a particular case of the federated learning, our work has a significance and purpose by itself and should be studied as a distinct field, given also the different practical application. Similarly, there are previous studies in HDC/VSA domain [17,28], which investigated the distributed scenario but all of them assumed some elements of centralization.…”

Section: Relation To Other Areasmentioning

confidence: 99%

On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks

Rosato

Panella

Osipov

et al. 2021

Advances in Computational Intelligence

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A change of the prevalent supervised learning techniques is foreseeable in the near future: from the complex, computational expensive algorithms to more flexible and elementary training ones. The strong revitalization of randomized algorithms can be framed in this prospect steering. We recently proposed a model for distributed classification based on randomized neural networks and hyperdimensional computing, which takes into account cost of information exchange between agents using compression. The use of compression is important as it addresses the issues related to the communication bottleneck, however, the original approach is rigid in the way the compression is used. Therefore, in this work, we propose a more flexible approach to compression and compare it to conventional compression algorithms, dimensionality reduction, and quantization techniques.

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Section: Relation To Other Areasmentioning

confidence: 99%

On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks

Rosato

Panella

Osipov

et al. 2021

Advances in Computational Intelligence

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“…However, due to the very large size of the brain's circuits, such neural activity patterns can only be modeled with points of high-dimensional space (e.g., D=10,000). The HD computing can perform the classification task using two main modules; encoding and associative search [23]. The encoding module maps input data into high-dimensional space, hypervector, then the training module combines all hypervectors in order to generate a binary hypervector representing each class.…”

Section: Search-based Pim Applicationsmentioning

confidence: 99%

Thermal-Aware Design and Management for Search-based In-Memory Acceleration

Zhou

Imani

Gupta

et al. 2019

Proceedings of the 56th Annual Design Automation Conference 2019

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Recently, Processing-In-Memory (PIM) techniques exploiting resistive RAM (ReRAM) have been used to accelerate various big data applications. ReRAM-based in-memory search is a powerful operation which efficiently finds required data in a large data set. However, such operations result in a large amount of current which may create serious thermal issues, especially in state-of-the-art 3D stacking chips. Therefore, designing PIM accelerators based on in-memory searches requires a careful consideration of temperature. In this work, we propose static and dynamic techniques to optimize the thermal behavior of PIM architectures running intensive in-memory search operations. Our experiments show the proposed design significantly reduces the peak chip temperature and dynamic management overhead. We test our proposed design in two important categories of applications which benefit from the search-based PIM acceleration-hyper-dimensional computing and database query. Validated experiments show that the proposed method can reduce the steady-state temperature by at least 15.3°C which extends the lifetime of the ReRAM device by 57.2% on average. Furthermore, the proposed fine-grained dynamic thermal management provides 17.6% performance improvement over stateof-the-art methods. CCS CONCEPTS • Computer systems organization → Architectures; • Hardware → Emerging technologies;

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“…For more details, we point the reader to [26], where the authors perform a comprehensive review of classification techniques based on HD computing. Indeed, HD computing was successfully applied in previous works on a limited set of research areas, like speech recognition [27,28], the internet of things [29][30][31][32], and two life-science related applications. These last attempts concern the detection of epileptogenic regions of the human brain from Intracranial Electroencephalography (iEEG) recordings [33] and pattern matching problems on DNA sequences for diagnostic purposes [34,35].…”

Section: Introductionmentioning

confidence: 99%

A Brain-Inspired Hyperdimensional Computing Approach for Classifying Massive DNA Methylation Data of Cancer

2020

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The recent advancements in cancer genomics have put under the spotlight DNA methylation, a genetic modification that regulates the functioning of the genome and whose modifications have an important role in tumorigenesis and tumor-suppression. Because of the high dimensionality and the enormous amount of genomic data that are produced through the last advancements in Next Generation Sequencing, it is very challenging to effectively make use of DNA methylation data in diagnostics applications, e.g., in the identification of healthy vs diseased samples. Additionally, state-of-the-art techniques are not fast enough to rapidly produce reliable results or efficient in managing those massive amounts of data. For this reason, we propose HD-classifier, an in-memory cognitive-based hyperdimensional (HD) supervised machine learning algorithm for the classification of tumor vs non tumor samples through the analysis of their DNA Methylation data. The approach takes inspiration from how the human brain is able to remember and distinguish simple and complex concepts by adopting hypervectors and no single numerical values. Exactly as the brain works, this allows for encoding complex patterns, which makes the whole architecture robust to failures and mistakes also with noisy data. We design and develop an algorithm and a software tool that is able to perform supervised classification with the HD approach. We conduct experiments on three DNA methylation datasets of different types of cancer in order to prove the validity of our algorithm, i.e., Breast Invasive Carcinoma (BRCA), Kidney renal papillary cell carcinoma (KIRP), and Thyroid carcinoma (THCA). We obtain outstanding results in terms of accuracy and computational time with a low amount of computational resources. Furthermore, we validate our approach by comparing it (i) to BIGBIOCL, a software based on Random Forest for classifying big omics datasets in distributed computing environments, (ii) to Support Vector Machine (SVM), and (iii) to Decision Tree state-of-the-art classification methods. Finally, we freely release both the datasets and the software on GitHub.

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A Framework for Collaborative Learning in Secure High-Dimensional Space

Cited by 82 publications

References 40 publications

On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks

On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks

Thermal-Aware Design and Management for Search-based In-Memory Acceleration

A Brain-Inspired Hyperdimensional Computing Approach for Classifying Massive DNA Methylation Data of Cancer

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