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Nika Mansouri

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Articles by Nika Mansouri

Computer Science > Hardware Architecture

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By Nika Mansouri, F. Nisa, Abdullah Giray
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Computer Science > Hardware Architecture

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By Nika Mansouri, Susana Rebolledo
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Conduit: Programmer-Transparent Near-Data Processing Using Multiple Compute-Capable Resources in Solid State Drives

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By Nika Mansouri, Andreas Kosmas
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing

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By Andreas Kosmas, Nika Mansouri
| arXiv
[Submitted on 19 Jun 2025] Title:REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing View a PDF of the paper titled REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing, by Kangqi Chen and 8 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) face an inherent challenge: their knowledge is confined to the data that they have been trained on.
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Ariadne: A Hotness-Aware and Size-Adaptive Compressed Swap Technique for Fast Application Relaunch and Reduced CPU Usage on Mobile Devices

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By Chun Jason, Nika Mansouri
| arXiv
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MegIS: High-Performance, Energy-Efficient, and Low-Cost Metagenomic Analysis with In-Storage Processing

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By Nika Mansouri, Meryem Banu
| arXiv
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[2404.11284] Amplifying Main Memory-Based Timing Covert and Side Channels using Processing-in-Memory Operations

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By F. Nisa, A. Giray, İsmail Emir, Nika Mansouri
| arXiv
[Submitted on 17 Apr 2024] Title:Amplifying Main Memory-Based Timing Covert and Side Channels using Processing-in-Memory Operations View a PDF of the paper titled Amplifying Main Memory-Based Timing Covert and Side Channels using Processing-in-Memory Operations, by Konstantinos Kanellopoulos and 8 other authors View PDF HTML (experimental) Abstract:The adoption of processing-in-memory (PiM) architectures has been gaining momentum because they provide high performance and low energy...
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Venice: Improving Solid-State Drive Parallelism at Low Cost via Conflict-Free Accesses

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By Juan Gomez, Juan Gómez, Nika Mansouri
| arXiv
arXiv:2305.07768 (cs) Download a PDF of the paper titled Venice: Improving Solid-State Drive Parallelism at Low Cost via Conflict-Free Accesses, by Rakesh Nadig and 8 other authors Download PDF Submission history From: Rakesh Nadig [ view email] [v1] Fri, 12 May 2023 21:25:26 UTC (595 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media...
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TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering

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By Meryem Banu, Nika Mansouri
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2211.04369] Accelerating Time Series Analysis via Processing using Non-Volatile Memories

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By Nika Mansouri
| arXiv
[Submitted on 8 Nov 2022 ( v1 ), last revised 12 Jul 2024 (this version, v3)] Title:Accelerating Time Series Analysis via Processing using Non-Volatile Memories View a PDF of the paper titled Accelerating Time Series Analysis via Processing using Non-Volatile Memories, by Ivan Fernandez and 8 other authors View PDF HTML (experimental) Abstract:Time Series Analysis (TSA) is a critical workload to extract valuable information from collections of sequential data, e.g., detecting anomalies in...
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RevaMp3D: Architecting the Processor Core and Cache Hierarchy for Systems with Monolithically-Integrated Logic and Memory

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By Nika Mansouri, Juan Gomez, Juan Gómez
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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