World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5693
World Ranking
12530
National Ranking
5086

Research.com Recognitions

  • 2019 - Fellow of Alfred P. Sloan Foundation

Overview

Reetuparna Das is affiliated with the University of Michigan-Ann Arbor in the United States. Their research spans multiple disciplines including biochemistry, genetics, molecular biology, and computer science, with notable contributions to molecular biology and artificial intelligence.

Their recent publications cover a range of topics in genomic sequencing, embedded computing, and machine learning applications in bioinformatics. Selected papers include:

  • Rapid Real-time Squiggle Classification for Read until using RawMap, 2023, Archives of Clinical and Biomedical Research
  • Rapid Real-time Squiggle Classification for Read Until Using RawMap, 2022, bioRxiv (Cold Spring Harbor Laboratory)
  • BitSET: Bit-Serial Early Termination for Computation Reduction in Convolutional Neural Networks, 2023, ACM Transactions on Embedded Computing Systems
  • A High-Throughput Pruning-Based Pair-Hidden-Markov-Model Hardware Accelerator for Next-Generation DNA Sequencing, 2020, IEEE Solid-State Circuits Letters
  • Hardware-friendly User-specific Machine Learning for Edge Devices, 2022, ACM Transactions on Embedded Computing Systems

The scientist's work is frequently published in venues such as bioRxiv (Cold Spring Harbor Laboratory), arXiv (Cornell University), Zenodo (CERN European Organization for Nuclear Research), ACM Transactions on Embedded Computing Systems, and Archives of Clinical and Biomedical Research.

Main areas of study include:

  • Biochemistry, Genetics and Molecular Biology
  • Computer Science

Subfields of focus are:

  • Molecular Biology
  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Hardware and Architecture

Research topics extensively covered encompass:

  • Genomics and Phylogenetic Studies
  • Advanced Memory and Neural Computing
  • Algorithms and Data Compression
  • Ferroelectric and Negative Capacitance Devices
  • Parallel Computing and Optimization Techniques
  • Machine Learning in Bioinformatics
  • Advanced Neural Network Applications

Reetuparna Das has collaborated frequently with a number of co-authors including Arun Subramaniyan, Satish Narayanasamy, Daichi Fujiki, David Blaauw, and Xiaowei Wang.

Among their contributions to academic literature, they have authored a book titled In-/Near-Memory Computing published in 2021 by Morgan & Claypool Publishers.

Recognition of their professional achievements includes being named a Fellow of the Alfred P. Sloan Foundation in 2019.

Best Publications

  • Neural cache: bit-serial in-cache acceleration of deep neural networks

    Charles Eckert;Xiaowei Wang;Jingcheng Wang;Arun Subramaniyan

  • A novel dimensionally-decomposed router for on-chip communication in 3D architectures

    Jongman Kim;Chrysostomos Nicopoulos;Dongkook Park;Reetuparna Das

  • Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism

    Jiecao Yu;Andrew Lukefahr;David Palframan;Ganesh Dasika

  • Compute Caches

    Shaizeen Aga;Supreet Jeloka;Arun Subramaniyan;Satish Narayanasamy

  • MIRA: A Multi-layered On-Chip Interconnect Router Architecture

    Dongkook Park;Soumya Eachempati;Reetuparna Das;Asit K. Mishra

  • Application-aware prioritization mechanisms for on-chip networks

    Reetuparna Das;Onur Mutlu;Thomas Moscibroda;Chita R. Das

  • Design and evaluation of a hierarchical on-chip interconnect for next-generation CMPs

    Reetuparna Das;Soumya Eachempati;Asit K. Mishra;Vijaykrishnan Narayanan

  • Aérgia: exploiting packet latency slack in on-chip networks

    Reetuparna Das;Onur Mutlu;Thomas Moscibroda;Chita R. Das

  • Composite Cores: Pushing Heterogeneity Into a Core

    Andrew Lukefahr;Shruti Padmanabha;Reetuparna Das;Faissal M. Sleiman

  • A 28-nm Compute SRAM With Bit-Serial Logic/Arithmetic Operations for Programmable In-Memory Vector Computing

    Jingcheng Wang;Xiaowei Wang;Charles Eckert;Arun Subramaniyan

  • Catnap: energy proportional multiple network-on-chip

    Reetuparna Das;Satish Narayanasamy;Sudhir K. Satpathy;Ronald G. Dreslinski

  • ANVIL: Software-Based Protection Against Next-Generation Rowhammer Attacks

    Zelalem Birhanu Aweke;Salessawi Ferede Yitbarek;Rui Qiao;Reetuparna Das

  • A case for dynamic frequency tuning in on-chip networks

    Asit K. Mishra;Reetuparna Das;Soumya Eachempati;Ravi Iyer

  • Application-to-core mapping policies to reduce memory system interference in multi-core systems

    R. Das;R. Ausavarungnirun;O. Mutlu;A. Kumar

  • Performance and power optimization through data compression in Network-on-Chip architectures

    R. Das;A.K. Mishra;C. Nicopoulos;Dongkook Park

  • 14.2 A Compute SRAM with Bit-Serial Integer/Floating-Point Operations for Programmable In-Memory Vector Acceleration

    Jingcheng Wang;Xiaowei Wang;Charles Eckert;Arun Subramaniyan

  • In-Memory Data Parallel Processor

    Daichi Fujiki;Scott Mahlke;Reetuparna Das

  • Scalpel

    Unknown

  • Power-Aware NoCs through Routing and Topology Reconfiguration

    Ritesh Parikh;Reetuparna Das;Valeria Bertacco

  • Swizzle-Switch Networks for Many-Core Systems

    K. Sewell;R. G. Dreslinski;T. Manville;S. Satpathy

  • Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks

    Charles Eckert;Xiaowei Wang;Jingcheng Wang;Arun Subramaniyan

Frequent Co-Authors

David Blaauw
David Blaauw University of Michigan–Ann Arbor
Satish Narayanasamy
Satish Narayanasamy University of Michigan–Ann Arbor
Scott Mahlke
Scott Mahlke University of Michigan–Ann Arbor
Ronald G. Dreslinski
Ronald G. Dreslinski University of Michigan–Ann Arbor
Chita R. Das
Chita R. Das Pennsylvania State University
Trevor Mudge
Trevor Mudge University of Michigan–Ann Arbor
Dennis Sylvester
Dennis Sylvester University of Michigan–Ann Arbor
Onur Mutlu
Onur Mutlu ETH Zurich
Ravi Iyer
Ravi Iyer Intel (United States)
Asit K. Mishra
Asit K. Mishra University College Cork

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