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Pranjali Jain

dblp:251/3153 · DBLP profile ↗
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3ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0003-2587-2027ORCID · corroborated

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

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 · 50% Integrated circuit design · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems › memory hierarchy
cache hierarchy
0.712023
HyGain: High-performance, Energy-efficient Hybrid Gain Cell-based Cache Hierarchy · ACM Trans. Archit. Code Optim. 2023
Integrated circuit design
low-power circuit design
0.712023
HyGain: High-performance, Energy-efficient Hybrid Gain Cell-based Cache Hierarchy · ACM Trans. Archit. Code Optim. 2023

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

staggered refresh · 0.7half VDD precharge · 0.7
YearPublicationVenuePosition
2025 Empowering E-Waste Recycling with Intelligent PCB Component Detection
Pranjali Jain, Madison Long, Claire Pemberton, Mariana Rosillo, Samantha West, Jonathan Balkind, Timothy Sherwood
COMPASS1
2023 HyGain: High-performance, Energy-efficient Hybrid Gain Cell-based Cache Hierarchy
abstract
In this article, we propose a “full-stack” solution to designing high-apacity and low-latency on-chip cache hierarchies by starting at the circuit level of the hardware design stack. We propose a novel half V DD precharge 2T Gain Cell (GC) design for the cache hierarchy. The GC has several desirable characteristics, including ~50% higher storage density and ~50% lower dynamic energy as compared to the traditional 6T SRAM, even after accounting for peripheral circuit overheads. We also demonstrate data retention time of 350 us (~17.5× of eDRAM) at 28 nm technology with V DD = 0.9V and temperature = 27°C that, combined with optimizations like staggered refresh, makes it an ideal candidate to architect all levels of on-chip caches. We show that compared to 6T SRAM, for a given area budget, GC-based caches, on average, provide 30% and 36% increase in IPC for single- and multi-programmed workloads, respectively, on contemporary workloads, including SPEC CPU 2017. We also observe dynamic energy savings of 42% and 34% for single- and multi-programmed workloads, respectively. Finally, in a quest to utilize the best of all worlds, we combine GC with STT-RAM to create hybrid hierarchies. We show that a hybrid hierarchy with GC caches at L1 and L2 and an LLC split between GC and STT-RAM is able to provide a 46% benefit in energy-delay product (EDP) as compared to an all-SRAM design, and 13% as compared to an all-GC cache hierarchy, averaged across multi-programmed workloads.
Sarabjeet Singh, Neelam Surana, Kailash Prasad, Pranjali Jain, Joycee Mekie, Manu Awasthi
ACM Trans. Archit. Code Optim.4
2020 NLPExplorer: Exploring the Universe of NLP Papers
Monarch Parmar, Naman Jain, Pranjali Jain, P. Jayakrishna Sahit, Soham Pachpande, Shruti Singh 0001, Mayank Singh 0001
ECIR (2)3