Ardavan Pedram

4.0k total citations · 1 hit paper
28 papers, 1.8k citations indexed

About

Ardavan Pedram is a scholar working on Hardware and Architecture, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Ardavan Pedram has authored 28 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Hardware and Architecture, 12 papers in Computer Networks and Communications and 7 papers in Electrical and Electronic Engineering. Recurrent topics in Ardavan Pedram's work include Parallel Computing and Optimization Techniques (19 papers), Interconnection Networks and Systems (11 papers) and Embedded Systems Design Techniques (11 papers). Ardavan Pedram is often cited by papers focused on Parallel Computing and Optimization Techniques (19 papers), Interconnection Networks and Systems (11 papers) and Embedded Systems Design Techniques (11 papers). Ardavan Pedram collaborates with scholars based in United States, Iran and Switzerland. Ardavan Pedram's co-authors include Mark Horowitz, Huizi Mao, Song Han, William J. Dally, Jing Pu, Xingyu Liu, Andreas Gerstlauer, Robert A. Geijn, Raghu Prabhakar and David Koeplinger and has published in prestigious journals such as IEEE Transactions on Computers, Chemical Physics and ACM SIGPLAN Notices.

In The Last Decade

Ardavan Pedram

26 papers receiving 1.8k 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.

EIE

2016 1.3k citations Song Han, Xingyu Liu et al. ACM SIGARCH Computer Architecture News profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ardavan Pedram United States	14	919	750	636	623	418	28	1.8k
Jing Pu United States	9	1.1k 1.2×	951 1.3×	709 1.1×	495 0.8×	277 0.7×	16	1.9k
Eriko Nurvitadhi United States	20	677 0.7×	695 0.9×	492 0.8×	698 1.1×	486 1.2×	75	1.7k
Ninghui Sun China	15	804 0.9×	711 0.9×	485 0.8×	665 1.1×	528 1.3×	63	1.7k
Angshuman Parashar United States	17	978 1.1×	1.1k 1.5×	588 0.9×	973 1.6×	560 1.3×	38	2.1k
Hyoukjun Kwon United States	16	703 0.8×	829 1.1×	414 0.7×	576 0.9×	339 0.8×	38	1.6k
Xiaobing Feng China	16	778 0.8×	727 1.0×	510 0.8×	562 0.9×	478 1.1×	100	1.7k
Anurag Mukkara United States	8	844 0.9×	680 0.9×	512 0.8×	533 0.9×	301 0.7×	9	1.4k
Rangharajan Venkatesan United States	21	1.0k 1.1×	1.8k 2.4×	682 1.1×	840 1.3×	540 1.3×	50	2.7k
Yakun Sophia Shao United States	19	587 0.6×	1.1k 1.4×	401 0.6×	1.2k 1.9×	705 1.7×	50	2.1k

Countries citing papers authored by Ardavan Pedram

Since Specialization

Citations

This map shows the geographic impact of Ardavan Pedram'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 Ardavan Pedram with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ardavan Pedram more than expected).

Fields of papers citing papers by Ardavan Pedram

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ardavan Pedram. 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 Ardavan Pedram. The network helps show where Ardavan Pedram may publish in the future.

Co-authorship network of co-authors of Ardavan Pedram

This figure shows the co-authorship network connecting the top 25 collaborators of Ardavan Pedram. A scholar is included among the top collaborators of Ardavan Pedram 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 Ardavan Pedram. Ardavan Pedram 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:

20 of 20 papers shown

1.

Pedram, Ardavan, et al.. (2022). Algorithm/architecture solutions to improve beyond uniform quantization in embedded DNN accelerators. Journal of Systems Architecture. 126. 102454–102454. 1 indexed citations

2.

Koeplinger, David, Matthew Feldman, Raghu Prabhakar, et al.. (2018). Spatial: a language and compiler for application accelerators. ACM SIGPLAN Notices. 53(4). 296–311. 15 indexed citations

3.

Koeplinger, David, Matthew Feldman, Raghu Prabhakar, et al.. (2018). Spatial: a language and compiler for application accelerators. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 296–311. 101 indexed citations

4.

Prabhakar, Raghu, David Koeplinger, Tian Zhao, et al.. (2018). Plasticine: A Reconfigurable Accelerator for Parallel Patterns. IEEE Micro. 38(3). 20–31. 13 indexed citations

5.

Li, Yuan-Fang & Ardavan Pedram. (2017). CATERPILLAR: Coarse Grain Reconfigurable Architecture for accelerating the training of Deep Neural Networks. 49. 1–10. 8 indexed citations

6.

Prabhakar, Raghu, David Koeplinger, Tian Zhao, et al.. (2017). Plasticine. 389–402. 144 indexed citations

7.

Han, Song, Xingyu Liu, Huizi Mao, et al.. (2016). EIE. ACM SIGARCH Computer Architecture News. 44(3). 243–254. 1277 indexed citations breakdown →

8.

Han, Song, Xingyu Liu, Huizi Mao, et al.. (2016). Deep compression and EIE: Efficient inference engine on compressed deep neural network. 30 indexed citations

9.

Pedram, Ardavan, et al.. (2016). Simulator calibration for accelerator-rich architecture studies. 88–95. 6 indexed citations

10.

Pedram, Ardavan, et al.. (2016). Improving energy efficiency of DRAM by exploiting half page row access. 1–12. 14 indexed citations

11.

Pedram, Ardavan, John D. McCalpin, & Andreas Gerstlauer. (2014). A Highly Efficient Multicore Floating-Point FFT Architecture Based on Hybrid Linear Algebra/FFT Cores. Journal of Signal Processing Systems. 77(1-2). 169–190. 6 indexed citations

12.

Pedram, Ardavan, John D. McCalpin, & Andreas Gerstlauer. (2013). Transforming a linear algebra core to an FFT accelerator. pp. 175–184. 15 indexed citations

13.

Pedram, Ardavan, Andreas Gerstlauer, & Robert A. Geijn. (2012). On the Efficiency of Register File versus Broadcast Interconnect for Collective Communications in Data-Parallel Hardware Accelerators. 19–26. 10 indexed citations

14.

Pedram, Ardavan, et al.. (2012). A Linear Algebra Core Design for Efficient Level-3 BLAS. 149–152. 10 indexed citations

15.

Pedram, Ardavan, Robert A. Geijn, & Andreas Gerstlauer. (2012). Codesign Tradeoffs for High-Performance, Low-Power Linear Algebra Architectures. IEEE Transactions on Computers. 61(12). 1724–1736. 42 indexed citations

16.

Pedram, Ardavan, Andreas Gerstlauer, & Robert A. Geijn. (2011). A high-performance, low-power linear algebra core. pp. 35–42. 19 indexed citations

17.

Daneshtalab, Masoud, et al.. (2007). Distributing Congestions in NoCs through a Dynamic Routing Algorithm based on Input and Output Selections. 50. 546–550. 14 indexed citations

18.

Behnejad, Hassan & Ardavan Pedram. (2006). Calculation of hydrogen’s thermal conductivity at moderate and high densities. Chemical Physics. 325(2-3). 351–358. 6 indexed citations

19.

Pedram, Ardavan, et al.. (2006). A High-Performance Memory-Efficient Parallel Hardware for Matrix Computation in Signal Processing Applications. 473–478. 1 indexed citations

20.

Pedram, Ardavan, Masoud Daneshtalab, & Sied Mehdi Fakhraie. (2006). An Efficient Parallel Architecture for Matrix Computations. 171–174.

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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