Debbie Marr

2.0k total citations · 1 hit paper
17 papers, 989 citations indexed

About

Debbie Marr is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Debbie Marr has authored 17 papers receiving a total of 989 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Hardware and Architecture, 8 papers in Computer Vision and Pattern Recognition and 7 papers in Electrical and Electronic Engineering. Recurrent topics in Debbie Marr's work include Parallel Computing and Optimization Techniques (12 papers), Advanced Neural Network Applications (8 papers) and Low-power high-performance VLSI design (3 papers). Debbie Marr is often cited by papers focused on Parallel Computing and Optimization Techniques (12 papers), Advanced Neural Network Applications (8 papers) and Low-power high-performance VLSI design (3 papers). Debbie Marr collaborates with scholars based in United States, United Kingdom and Australia. Debbie Marr's co-authors include Eriko Nurvitadhi, Jaewoong Sim, Ganesh Venkatesh, Asit Mishra, David Sheffield, Srivatsan Krishnan, Suchit Subhaschandra, Duncan J. M. Moss, Guy Boudoukh and Randy Huang and has published in prestigious journals such as ACM SIGPLAN Notices, IEEE Computer Architecture Letters and arXiv (Cornell University).

In The Last Decade

Debbie Marr

17 papers receiving 958 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?

2017 287 citations Eriko Nurvitadhi, Ganesh Venkatesh et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Debbie Marr United States	12	483	471	350	298	191	17	989
Hiroki Nakahara Japan	16	511 1.1×	485 1.0×	321 0.9×	202 0.7×	110 0.6×	98	971
Jaeha Kung South Korea	15	536 1.1×	305 0.6×	230 0.7×	223 0.7×	144 0.8×	51	827
Fengbin Tu China	18	820 1.7×	561 1.2×	398 1.1×	250 0.8×	127 0.7×	64	1.3k
Tong Geng United States	18	339 0.7×	368 0.8×	398 1.1×	289 1.0×	213 1.1×	70	951
Naveen Suda United States	10	624 1.3×	686 1.5×	411 1.2×	191 0.6×	120 0.6×	15	1.1k
Paul N. Whatmough United States	17	820 1.7×	478 1.0×	515 1.5×	286 1.0×	151 0.8×	39	1.3k
Hardik Sharma United States	6	518 1.1×	493 1.0×	362 1.0×	280 0.9×	139 0.7×	13	918
Hanrui Wang United States	15	482 1.0×	456 1.0×	623 1.8×	301 1.0×	205 1.1×	43	1.3k
Yingyan Lin United States	19	393 0.8×	522 1.1×	472 1.3×	141 0.5×	187 1.0×	81	1.1k

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-authorship network of co-authors of Debbie Marr

This figure shows the co-authorship network connecting the top 25 collaborators of Debbie Marr. A scholar is included among the top collaborators of Debbie Marr based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Debbie Marr. Debbie Marr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

17 of 17 papers shown

1.

Nurvitadhi, Eriko, Ali Jafari, Andrew Boutros, et al.. (2019). Why Compete When You Can Work Together: FPGA-ASIC Integration for Persistent RNNs. 199–207. 43 indexed citations

2.

Nurvitadhi, Eriko, Ali Jafari, Andrew Boutros, et al.. (2019). Evaluating and Enhancing Intel® Stratix® 10 FPGAs for Persistent Real-Time AI. 119–119. 4 indexed citations

3.

Mishra, Asit, Eriko Nurvitadhi, Jeffrey Cook, & Debbie Marr. (2018). WRPN: Wide Reduced-Precision Networks. arXiv (Cornell University). 25 indexed citations

4.

Nurvitadhi, Eriko, Jeffrey Cook, Asit Mishra, et al.. (2018). In-Package Domain-Specific ASICs for Intel® Stratix® 10 FPGAs. 287–287. 8 indexed citations

5.

Moss, Duncan J. M., Srivatsan Krishnan, Eriko Nurvitadhi, et al.. (2018). A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform. 107–116. 60 indexed citations

6.

Nurvitadhi, Eriko, Jeffrey Cook, Asit Mishra, et al.. (2018). In-Package Domain-Specific ASICs for Intel® Stratix® 10 FPGAs: A Case Study of Accelerating Deep Learning Using TensorTile ASIC. 106–1064. 18 indexed citations

7.

Nurvitadhi, Eriko, Ganesh Venkatesh, Jaewoong Sim, et al.. (2017). Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?. 5–14. 287 indexed citations breakdown →

8.

Mishra, Asit, Eriko Nurvitadhi, Ganesh Venkatesh, Jonathan P. Pearce, & Debbie Marr. (2017). Fine-grained accelerators for sparse machine learning workloads. 635–640. 17 indexed citations

9.

Venkatesh, Ganesh, Eriko Nurvitadhi, & Debbie Marr. (2017). Accelerating Deep Convolutional Networks using low-precision and sparsity. 2861–2865. 76 indexed citations

10.

Moss, Duncan J. M., Eriko Nurvitadhi, Jaewoong Sim, et al.. (2017). High performance binary neural networks on the Xeon+FPGA™ platform. 1–4. 47 indexed citations

11.

Nurvitadhi, Eriko, Davor Capalija, Andrew C. Ling, et al.. (2017). Customizable FPGA OpenCL matrix multiply design template for deep neural networks. 259–262. 10 indexed citations

12.

Nurvitadhi, Eriko, David Sheffield, Jaewoong Sim, et al.. (2016). Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC. 77–84. 223 indexed citations

13.

Nurvitadhi, Eriko, Jaewoong Sim, David Sheffield, et al.. (2016). Accelerating recurrent neural networks in analytics servers: Comparison of FPGA, CPU, GPU, and ASIC. 1–4. 129 indexed citations

14.

Nurvitadhi, Eriko, Asit Mishra, Yu Wang, Ganesh Venkatesh, & Debbie Marr. (2016). Hardware Accelerator for Analytics of Sparse Data. 1616–1621. 12 indexed citations

15.

Hashemi, Milad, Debbie Marr, Doug Carmean, & Yale N. Patt. (2015). Efficient Execution of Bursty Applications. IEEE Computer Architecture Letters. 15(2). 85–88. 4 indexed citations

16.

Nurvitadhi, Eriko, Asit Mishra, & Debbie Marr. (2015). A sparse matrix vector multiply accelerator for support vector machine. 25 indexed citations

17.

Marr, Debbie. (2014). Session details: Heterogeneous computing. ACM SIGPLAN Notices. 49(4). 1 indexed citations

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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