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Abhilasha Jain

dblp:147/4879 · also Abhilasha · DBLP profile ↗
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3ranked-venue papers
0as first author
2since 2021 · last 2024
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 100%
Software engineering, system software, and programming languages
1 paper
Operating systems · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
DRAM
0.312018
A Case for Richer Cross-Layer Abstractions: Bridging the Semantic Gap with Expressive Memory · ISCA 2018
Memory systems
page placement
0.312018
A Case for Richer Cross-Layer Abstractions: Bridging the Semantic Gap with Expressive Memory · ISCA 2018
Operating systems › resource management
memory management
0.112018
A Case for Richer Cross-Layer Abstractions: Bridging the Semantic Gap with Expressive Memory · ISCA 2018

Methods — techniques the papers use, named apart from their topics

cross-layer interface design · 0.7
YearPublicationVenuePosition
2024 Enhancing ALPR: a two stage YOLO model with data augmentation for improved accuracy and robustness
Swati Bansal, Abhilasha Jain, Shivam Ojha, Hemant Walia
Multim. Tools Appl.2
2022 A real time face mask detection system using convolutional neural network
Hiten Goyal, Karanveer Sidana, Charanjeet, Abhilasha Jain, Swati Jindal
Multim. Tools Appl.4
2018 A Case for Richer Cross-Layer Abstractions: Bridging the Semantic Gap with Expressive Memory
abstract
This paper makes a case for a new cross-layer interface, Expressive Memory (XMem), to communicate higher-level program semantics from the application to the system software and hardware architecture. XMem provides (i) a flexible and extensible abstraction, called an Atom, enabling the application to express key program semantics in terms of how the program accesses data and the attributes of the data itself, and (ii) new cross-layer interfaces to make the expressed higher-level information available to the underlying OS and architecture. By providing key information that is otherwise unavailable, XMem exposes a new, rich view of the program data to the OS and the different architectural components that optimize memory system performance (e.g., caches, memory controllers). By bridging the semantic gap between the application and the underlying memory resources, XMem provides two key benefits. First, it enables architectural/system-level techniques to leverage key program semantics that are challenging to predict or infer. Second, it improves the efficacy and portability of software optimizations by alleviating the need to tune code for specific hardware resources (e.g., cache space). While XMem is designed to enhance and enable a wide range of memory optimizations, we demonstrate the benefits of XMem using two use cases: (i) improving the performance portability of software-based cache optimization by expressing the semantics of data locality in the optimization and (ii) improving the performance of OS-based page placement in DRAM by leveraging the semantics of data structures and their access properties.
Nandita Vijaykumar, Abhilasha Jain, Diptesh Majumdar, Kevin Hsieh, Gennady Pekhimenko, Eiman Ebrahimi, Nastaran Hajinazar, Phillip B. Gibbons, Onur Mutlu
ISCA2