Debbie Marr

2.1k citations
17 papers · 999 · 1 hit paper · h-index 12

Impact in

Papers in

Co-authors
Eriko Nurvitadhi (15 shared papers)Jaewoong Sim (7 shared papers)Ganesh Venkatesh (5 shared papers)Asit Mishra (10 shared papers)David Sheffield (2 shared papers)Duncan J. M. Moss (4 shared papers)Suchit Subhaschandra (4 shared papers)Srivatsan Krishnan (3 shared papers)
Journals
IEEE Computer Architecture Letters (1 paper)ACM SIGPLAN Notices (1 paper)arXiv (Cornell University) (1 paper)
Partner nations
United StatesUnited KingdomAustralia

In The Last Decade

Debbie Marr

17 papers receiving 966 citations

Debbie Marr's Hit Papers

Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? 2017 · 293 citations
2930+3+6Years since publication50100150200250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

  • it has ≥500 total citations;
  • it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
  • it reaches the top citation threshold in at least one of its specific research topics.
  1. 2017 Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?

Peers

Debbie Marr
Comparison fields: 5 of 74
  • Hardware and Architecture 298
  • Computational Mathematics 23
  • Computer Vision and Pattern Recognition 472
  • Artificial Intelligence 352
  • Electrical and Electronic Engineering 486
Replace Hardik Sharma with:
Hardik Sharma United States
Fengbin Tu China
Tong Geng United States
Jorge Albericio Canada
Ananda Samajdar United States
Ganesh Dasika United States
Yuwei Hu United States
Shengen Yan China
Debbie Marr relative to Hardik Sharma United States Hardik Sharma's profile →
Citations per field
00.5×1.7×
Hardik Sharma · 1×
Citations per year

Countries citing papers authored by Debbie Marr

Since Specialization
Citations

This map shows the geographic impact of Debbie Marr's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Debbie Marr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debbie Marr more than expected).

Fields of papers citing papers by Debbie Marr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Debbie Marr. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Debbie Marr. The network helps show where Debbie Marr may publish in the future.

Co-authors

The 25 scholars most cited alongside Debbie Marr, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Debbie Marr Line = papers co-authored together Debbie Marr links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?
Eriko Nurvitadhi, Ganesh Venkatesh, Jaewoong Sim, Debbie Marr, Randy Huang, Jason Ong Gee Hock, Yeong Tat Liew, (unknown), Duncan J. M. Moss, Suchit Subhaschandra, Guy Boudoukh
Hit paper breakdown →		2017293
2
Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC
Eriko Nurvitadhi, David Sheffield, Jaewoong Sim, Asit Mishra, Ganesh Venkatesh, Debbie Marr
2016224
3
Accelerating recurrent neural networks in analytics servers: Comparison of FPGA, CPU, GPU, and ASIC
Eriko Nurvitadhi, Jaewoong Sim, David Sheffield, Asit Mishra, Srivatsan Krishnan, Debbie Marr
2016130
4
Accelerating Deep Convolutional Networks using low-precision and sparsity
Ganesh Venkatesh, Eriko Nurvitadhi, Debbie Marr
201776
5
A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform
Duncan J. M. Moss, Srivatsan Krishnan, Eriko Nurvitadhi, Piotr Ratuszniak, Christopher N. Johnson, Jaewoong Sim, Asit Mishra, Debbie Marr, Suchit Subhaschandra, Philip H. W. Leong
201860
6
High performance binary neural networks on the Xeon+FPGA™ platform
Duncan J. M. Moss, Eriko Nurvitadhi, Jaewoong Sim, Asit Mishra, Debbie Marr, Suchit Subhaschandra, Philip H. W. Leong
201747
7
Why Compete When You Can Work Together: FPGA-ASIC Integration for Persistent RNNs
Eriko Nurvitadhi, Dongup Kwon, Ali Jafari, Andrew Boutros, Jaewoong Sim, Phillip Tomson, H. Ekin Sumbul, Gregory Chen, Phil Knag, Raghavan Kumar, Ram Krishnamurthy, Sergey Gribok, Bogdan Pasca, Martin Langhammer, Debbie Marr, Aravind Dasu
201943
8
A sparse matrix vector multiply accelerator for support vector machine
Eriko Nurvitadhi, Asit Mishra, Debbie Marr
201527
9
WRPN: Wide Reduced-Precision Networks
arXiv (Cornell University) ·Asit Mishra, Eriko Nurvitadhi, Jeffrey Cook, Debbie Marr
201825
10
In-Package Domain-Specific ASICs for Intel® Stratix® 10 FPGAs: A Case Study of Accelerating Deep Learning Using TensorTile ASIC
Eriko Nurvitadhi, Jeffrey Cook, Asit Mishra, Debbie Marr, Kevin Nealis, Philip Colangelo, Andrew C. Ling, Davor Capalija, Utku Aydonat, Aravind Dasu, Sergey Shumarayev
201818
11
Fine-grained accelerators for sparse machine learning workloads
Asit Mishra, Eriko Nurvitadhi, Ganesh Venkatesh, Jonathan P. Pearce, Debbie Marr
201717
12
Hardware Accelerator for Analytics of Sparse Data
Eriko Nurvitadhi, Asit Mishra, Yu Wang, Ganesh Venkatesh, Debbie Marr
201612
13
Customizable FPGA OpenCL matrix multiply design template for deep neural networks
Jack Yinger, Eriko Nurvitadhi, Davor Capalija, Andrew C. Ling, Debbie Marr, Srivatsan Krishnan, Duncan J. M. Moss, Suchit Subhaschandra
201710
14
In-Package Domain-Specific ASICs for Intel® Stratix® 10 FPGAs
Eriko Nurvitadhi, Jeffrey Cook, Asit Mishra, Debbie Marr, Kevin Nealis, Philip Colangelo, Andrew C. Ling, Davor Capalija, Utku Aydonat, Sergey Shumarayev, Aravind Dasu
20188
15
Evaluating and Enhancing Intel® Stratix® 10 FPGAs for Persistent Real-Time AI
Eriko Nurvitadhi, Dongup Kwon, Ali Jafari, Andrew Boutros, Jaewoong Sim, Phillip Tomson, H. Ekin Sumbul, Gregory Chen, Phil Knag, Raghavan Kumar, Ram Krishnamurthy, Debbie Marr, Sergey Gribok, Bogdan Pasca, Martin Langhammer, Aravind Dasu
20194
16
Efficient Execution of Bursty Applications
IEEE Computer Architecture Letters ·Milad Hashemi, Debbie Marr, Doug Carmean, Yale N. Patt
20154
17
Session details: Heterogeneous computing
ACM SIGPLAN Notices ·Debbie Marr
20141

About Debbie Marr

Debbie Marr is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Computer Networks and Communications and Artificial Intelligence, having authored 17 papers that have together received 999 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (12 papers), Advanced Neural Network Applications (8 papers), Embedded Systems Design Techniques (3 papers), Advanced Data Storage Technologies (3 papers), Low-power high-performance VLSI design (3 papers), Ferroelectric and Negative Capacitance Devices (2 papers), Cloud Computing and Resource Management (2 papers) and Advanced Memory and Neural Computing (2 papers). The work is most often cited by research in Hardware and Architecture (298 citations), Computational Mathematics (23 citations), Computer Vision and Pattern Recognition (472 citations), Artificial Intelligence (352 citations) and Electrical and Electronic Engineering (486 citations). Debbie Marr has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Eriko Nurvitadhi, Jaewoong Sim, Ganesh Venkatesh, Asit Mishra, David Sheffield, Duncan J. M. Moss, Suchit Subhaschandra, Srivatsan Krishnan, Guy Boudoukh and Randy Huang. Their work appears in journals such as IEEE Computer Architecture Letters, ACM SIGPLAN Notices and arXiv (Cornell University).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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