Jorge Albericio

2.1k total citations · 1 hit paper
20 papers, 1.2k citations indexed

About

Jorge Albericio is a scholar working on Hardware and Architecture, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jorge Albericio has authored 20 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Hardware and Architecture, 10 papers in Computer Networks and Communications and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jorge Albericio's work include Parallel Computing and Optimization Techniques (15 papers), Advanced Data Storage Technologies (10 papers) and Advanced Neural Network Applications (9 papers). Jorge Albericio is often cited by papers focused on Parallel Computing and Optimization Techniques (15 papers), Advanced Data Storage Technologies (10 papers) and Advanced Neural Network Applications (9 papers). Jorge Albericio collaborates with scholars based in Canada, United Kingdom and Spain. Jorge Albericio's co-authors include Andreas Moshovos, Patrick Judd, Tayler Hetherington, Tor M. Aamodt, Natalie Enright Jerger, Joshua San Miguel, Víctor Viñals, Pablo Ibáñez, J.M. Llaberia and Aamer Jaleel and has published in prestigious journals such as Computer, IEEE Micro and Parallel Computing.

In The Last Decade

Jorge Albericio

20 papers receiving 1.1k 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.

Cnvlutin

2016 429 citations Jorge Albericio, Patrick Judd 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
Jorge Albericio Canada	12	677	664	433	414	237	20	1.2k
Anurag Mukkara United States	8	844 1.2×	680 1.0×	533 1.2×	512 1.2×	301 1.3×	9	1.4k
Shengen Yan China	14	495 0.7×	332 0.5×	371 0.9×	252 0.6×	285 1.2×	32	946
Patrick Judd Canada	10	800 1.2×	674 1.0×	283 0.7×	480 1.2×	106 0.4×	15	1.1k
Hardik Sharma United States	6	493 0.7×	518 0.8×	280 0.6×	362 0.9×	139 0.6×	13	918
Ananda Samajdar United States	12	422 0.6×	549 0.8×	364 0.8×	287 0.7×	212 0.9×	17	941
Liqiang Lu China	13	647 1.0×	546 0.8×	314 0.7×	306 0.7×	118 0.5×	38	1.1k
Naveen Suda United States	10	686 1.0×	624 0.9×	191 0.4×	411 1.0×	120 0.5×	15	1.1k
Tayler Hetherington Canada	9	705 1.0×	711 1.1×	768 1.8×	423 1.0×	581 2.5×	13	1.6k
Tong Geng United States	18	368 0.5×	339 0.5×	289 0.7×	398 1.0×	213 0.9×	70	951

Countries citing papers authored by Jorge Albericio

Since Specialization

Citations

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

Fields of papers citing papers by Jorge Albericio

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jorge Albericio

This figure shows the co-authorship network connecting the top 25 collaborators of Jorge Albericio. A scholar is included among the top collaborators of Jorge Albericio 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 Jorge Albericio. Jorge Albericio is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Mahmoud, Mostafa, Zissis Poulos, Alberto Delmás Lascorz, et al.. (2019). Accelerating Image-Sensor-Based Deep Learning Applications. IEEE Micro. 39(5). 26–35. 1 indexed citations

2.

Moshovos, Andreas, Jorge Albericio, Patrick Judd, et al.. (2018). Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning. abs 1706 504. 356–360. 2 indexed citations

3.

Moshovos, Andreas, Jorge Albericio, Patrick Judd, et al.. (2018). Exploiting Typical Values to Accelerate Deep Learning. Computer. 51(5). 18–30. 1 indexed citations

4.

Moshovos, Andreas, Jorge Albericio, Patrick Judd, et al.. (2018). Value-Based Deep-Learning Acceleration. IEEE Micro. 38(1). 41–55. 3 indexed citations

5.

Judd, Patrick, Jorge Albericio, Tayler Hetherington, et al.. (2017). Proteus: Exploiting precision variability in deep neural networks. Parallel Computing. 73. 40–51. 12 indexed citations

6.

Miguel, Joshua San, Jorge Albericio, Natalie Enright Jerger, & Aamer Jaleel. (2016). The bunker cache for spatio-value approximation. International Symposium on Microarchitecture. 1–12. 27 indexed citations

7.

Albericio, Jorge, Patrick Judd, Tayler Hetherington, et al.. (2016). Cnvlutin. ACM SIGARCH Computer Architecture News. 44(3). 1–13. 429 indexed citations breakdown →

8.

Seznec, André, Joshua San Miguel, & Jorge Albericio. (2016). Practical Multidimensional Branch Prediction. IEEE Micro. 36(3). 10–19. 4 indexed citations

9.

Judd, Patrick, Jorge Albericio, Tayler Hetherington, Tor M. Aamodt, & Andreas Moshovos. (2016). Stripes: Bit-serial deep neural network computing. 1–12. 228 indexed citations

10.

Miguel, Joshua San, Jorge Albericio, Natalie Enright Jerger, & Aamer Jaleel. (2016). The Bunker Cache for spatio-value approximation. 1–12. 18 indexed citations

11.

Albericio, Jorge, Patrick Judd, Tayler Hetherington, et al.. (2016). Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing. 1–13. 162 indexed citations

12.

Judd, Patrick, Jorge Albericio, & Andreas Moshovos. (2016). Stripes: Bit-Serial Deep Neural Network Computing. IEEE Computer Architecture Letters. 16(1). 80–83. 45 indexed citations

13.

Judd, Patrick, Jorge Albericio, Tayler Hetherington, et al.. (2016). Proteus. 1–12. 54 indexed citations

14.

Miguel, Joshua San, Jorge Albericio, Andreas Moshovos, & Natalie Enright Jerger. (2015). Doppelgänger. 50–61. 87 indexed citations

15.

Seznec, André, Joshua San Miguel, & Jorge Albericio. (2015). The inner most loop iteration counter. 347–357. 11 indexed citations

16.

Albericio, Jorge, Joshua San Miguel, Natalie Enright Jerger, & Andreas Moshovos. (2014). Wormhole: Wisely Predicting Multidimensional Branches. 9. 509–520. 10 indexed citations

17.

Judd, Patrick, Di Wu, Jorge Albericio, et al.. (2014). Evaluating the memory system behavior of smartphone workloads. 83–92. 9 indexed citations

18.

Albericio, Jorge, Pablo Ibáñez, Víctor Viñals, & J.M. Llaberia. (2013). The reuse cache. 310–321. 34 indexed citations

19.

Albericio, Jorge, Pablo Ibáñez, Víctor Viñals, & J.M. Llaberia. (2013). Exploiting reuse locality on inclusive shared last-level caches. ACM Transactions on Architecture and Code Optimization. 9(4). 1–19. 11 indexed citations

20.

Albericio, Jorge, et al.. (2012). ABS. ACM Transactions on Architecture and Code Optimization. 8(4). 1–20. 19 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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