Angelo Garofalo

837 total citations
29 papers, 286 citations indexed

About

Angelo Garofalo is a scholar working on Electrical and Electronic Engineering, Hardware and Architecture and Computer Vision and Pattern Recognition. According to data from OpenAlex, Angelo Garofalo has authored 29 papers receiving a total of 286 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Electrical and Electronic Engineering, 14 papers in Hardware and Architecture and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Angelo Garofalo's work include Advanced Neural Network Applications (11 papers), Parallel Computing and Optimization Techniques (10 papers) and Advanced Memory and Neural Computing (8 papers). Angelo Garofalo is often cited by papers focused on Advanced Neural Network Applications (11 papers), Parallel Computing and Optimization Techniques (10 papers) and Advanced Memory and Neural Computing (8 papers). Angelo Garofalo collaborates with scholars based in Italy, Switzerland and United Arab Emirates. Angelo Garofalo's co-authors include Luca Benini, Davide Rossi, Francesco Conti, Giuseppe Tagliavini, Alfio Di Mauro, Manuele Rusci, Geethan Karunaratne, Irem Boybat, Ernesto Sánchez and Riccardo Cantoro and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Journal of Solid-State Circuits and IEEE Transactions on Computers.

In The Last Decade

Angelo Garofalo

25 papers receiving 283 citations

Peers

Angelo Garofalo

EEE

HA

CVPR

AI

CNC

Linyan Mei Belgium

Sheng-Chun Kao United States

Jon J. Pimentel United States

Joonho Song South Korea

Alec Lu Canada

Philipp Gysel United States

Davor Capalija Canada

Mengshu Sun United States

Manoj Alwani India

Linyan Mei Belgium

Angelo Garofalo

187 ×1.1

EEE

83 ×0.7

HA

122 ×1.2

CVPR

66 ×0.9

AI

53 ×1.0

CNC

Citations per year, relative to Angelo Garofalo Angelo Garofalo (= 1×) peers Linyan Mei

Countries citing papers authored by Angelo Garofalo

Since Specialization

Citations

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

Fields of papers citing papers by Angelo Garofalo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Angelo Garofalo

This figure shows the co-authorship network connecting the top 25 collaborators of Angelo Garofalo. A scholar is included among the top collaborators of Angelo Garofalo 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 Angelo Garofalo. Angelo Garofalo 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.

Conti, Francesco, Angelo Garofalo, Davide Rossi, Giuseppe Tagliavini, & Luca Benini. (2025). Open Source Heterogeneous SoCs for Artificial Intelligence: The PULP Platform experience. IEEE Solid-State Circuits Magazine. 17(2). 49–60. 1 indexed citations

2.

Garofalo, Angelo, et al.. (2025). A Flexible Template for Edge Generative AI With High-Accuracy Accelerated Softmax and GELU. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 15(2). 200–216. 2 indexed citations

3.

Garofalo, Angelo, et al.. (2025). Quadrilatero: A RISC-V programmable matrix coprocessor for low-power edge applications. ArXiv.org. 66–69. 1 indexed citations

4.

Conti, Francesco, et al.. (2025). MXDOTP: A RISC-V ISA Extension for Enabling Microscaling (MX) Floating-Point Dot Products. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 81–84.

5.

Bartolini, Andrea, et al.. (2024). Unleashing OpenTitan’s Potential: a Silicon-Ready Embedded Secure Element for Root of Trust and Cryptographic Offloading. ACM Transactions on Embedded Computing Systems. 24(5). 1–29.

6.

Benz, Thomas, et al.. (2024). AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 1–6. 3 indexed citations

7.

Garofalo, Angelo, et al.. (2023). Cyber Security aboard Micro Aerial Vehicles: An OpenTitan-based Visual Communication Use Case. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 1–5. 6 indexed citations

8.

Garofalo, Angelo, Giuseppe Tagliavini, Francesco Conti, et al.. (2023). Dustin: A 16-Cores Parallel Ultra-Low-Power Cluster With 2b-to-32b Fully Flexible Bit-Precision and Vector Lockstep Execution Mode. IEEE Transactions on Circuits and Systems I Regular Papers. 70(6). 2450–2463. 10 indexed citations

9.

Conti, Francesco, et al.. (2023). Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC With 2–8 b DNN Acceleration and 30%-Boost Adaptive Body Biasing. IEEE Journal of Solid-State Circuits. 59(1). 128–142. 16 indexed citations

10.

Burrello, Alessio, et al.. (2023). A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 1–6. 7 indexed citations

11.

Tagliavini, Giuseppe, Angelo Garofalo, Francesco Conti, et al.. (2023). End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 1–6. 1 indexed citations

12.

Rossi, Davide, Angelo Garofalo, Alfio Di Mauro, et al.. (2023). 22.1 A 12.4TOPS/W @ 136GOPS AI-IoT System-on-Chip with 16 RISC-V, 2-to-8b Precision-Scalable DNN Acceleration and 30%-Boost Adaptive Body Biasing. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 21–23. 21 indexed citations

13.

Garofalo, Angelo, et al.. (2022). DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training. SHILAP Revista de lepidopterología. 2. 231–243. 14 indexed citations

14.

Garofalo, Angelo, Francesco Conti, Geethan Karunaratne, et al.. (2022). A Heterogeneous In-Memory Computing Cluster for Flexible End-to-End Inference of Real-World Deep Neural Networks. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 12(2). 422–435. 26 indexed citations

15.

Garofalo, Angelo, Giuseppe Tagliavini, Francesco Conti, Luca Benini, & Davide Rossi. (2021). XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V Based IoT End Nodes. IEEE Transactions on Emerging Topics in Computing. 9(3). 1489–1505. 33 indexed citations

16.

Mach, Stefan, Simone Benatti, Angelo Garofalo, et al.. (2021). A Low-Power Transprecision Floating-Point Cluster for Efficient Near-Sensor Data Analytics. IEEE Transactions on Parallel and Distributed Systems. 33(5). 1038–1053. 11 indexed citations

17.

Garofalo, Angelo, Alfio Di Mauro, Francesco Conti, et al.. (2021). A 1.15 TOPS/W, 16-Cores Parallel Ultra-Low Power Cluster with 2b-to-32b Fully Flexible Bit-Precision and Vector Lockstep Execution Mode. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 267–270. 10 indexed citations

18.

Garofalo, Angelo, Giuseppe Tagliavini, Francesco Conti, Luca Benini, & Davide Rossi. (2021). XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V based IoT End Nodes. 53–53. 3 indexed citations

19.

Garofalo, Angelo, et al.. (2020). A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inference. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 512–517. 23 indexed citations

20.

Ruospo, Annachiara, Riccardo Cantoro, Ernesto Sánchez, et al.. (2019). On-line Testing for Autonomous Systems driven by RISC-V Processor Design Verification. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 9 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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