Martino Dazzi

939 total citations · 1 hit paper
12 papers, 446 citations indexed

About

Martino Dazzi is a scholar working on Electrical and Electronic Engineering, Biomedical Engineering and Cellular and Molecular Neuroscience. According to data from OpenAlex, Martino Dazzi has authored 12 papers receiving a total of 446 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Electrical and Electronic Engineering, 3 papers in Biomedical Engineering and 1 paper in Cellular and Molecular Neuroscience. Recurrent topics in Martino Dazzi's work include Ferroelectric and Negative Capacitance Devices (6 papers), Advanced Memory and Neural Computing (6 papers) and Advancements in PLL and VCO Technologies (5 papers). Martino Dazzi is often cited by papers focused on Ferroelectric and Negative Capacitance Devices (6 papers), Advanced Memory and Neural Computing (6 papers) and Advancements in PLL and VCO Technologies (5 papers). Martino Dazzi collaborates with scholars based in Switzerland, Italy and Austria. Martino Dazzi's co-authors include Abu Sebastian, Evangelos Eleftheriou, Irem Boybat, Christophe Piveteau, S. R. Nandakumar, Manuel Le Gallo, Vinay Joshi, Bipin Rajendran, Pier Andrea Francese and Luca Benini and has published in prestigious journals such as IEEE Transactions on Computers, IBM Journal of Research and Development and IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

In The Last Decade

Martino Dazzi

12 papers receiving 435 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.

Accurate deep neural network inference using computational phase-change memory

2020 300 citations Vinay Joshi, Manuel Le Gallo et al. Repository for Publications and Research Data (ETH Zurich) profile →

Peers

Countries citing papers authored by Martino Dazzi

Since Specialization

Citations

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

Fields of papers citing papers by Martino Dazzi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Martino Dazzi

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

All Works

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12 of 12 papers shown

#	Work	Indexed citations
1	ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning 2022 ·IEEE Transactions on Computers ·(unknown), Irem Boybat, (unknown), Martino Dazzi, Alexandre Levisse, Giovanni Ansaloni, Marina Zapater, Abu Sebastian, David Atienza	7
2	Accelerating Inference of Convolutional Neural Networks Using In-memory Computing 2021 ·Frontiers in Computational Neuroscience ·Martino Dazzi, Abu Sebastian, Luca Benini, Evangelos Eleftheriou	15
3	A Fully Integrated 5-mW, 0.8-Gbps Energy-Efficient Chip-to-Chip Data Link for Ultralow-Power IoT End-Nodes in 65-nm CMOS 2021 ·IEEE Transactions on Very Large Scale Integration (VLSI) Systems ·(unknown), (unknown), Martino Dazzi, Pierpaolo Palestri, Simone Benatti, Luca Benini, Davide Rossi	1
4	Efficient Pipelined Execution of CNNs Based on In-Memory Computing and Graph Homomorphism Verification 2021 ·IEEE Transactions on Computers ·Martino Dazzi, Abu Sebastian, Thomas Parnell, Pier Andrea Francese, Luca Benini, Evangelos Eleftheriou	9
5	Accurate deep neural network inference using computational phase-change memory breakdown → 2020 ·Repository for Publications and Research Data (ETH Zurich) ·Vinay Joshi, Manuel Le Gallo, (unknown), Irem Boybat, S. R. Nandakumar, Christophe Piveteau, Martino Dazzi, Bipin Rajendran, Abu Sebastian, Evangelos Eleftheriou	300
6	A Simple Modelling Tool for Fast Combined Simulation of Interconnections, Inter-Symbol Interference and Equalization in High-Speed Serial Interfaces for Chip-to-Chip Communications 2020 ·Advances in Science Technology and Engineering Systems Journal ·(unknown), (unknown), (unknown), Martino Dazzi, (unknown), (unknown), (unknown), (unknown), (unknown), Andrea Cristofoli, (unknown), Roberto Nonis, Pierpaolo Palestri	1
7	A simple and fast tool for the modelling of inter-symbol interference and equalization in high-speed chip-to-chip interfaces 2019 ·Institutional Research Information System (University of Udine) ·(unknown), Martino Dazzi, (unknown), (unknown), (unknown), (unknown), Andrea Cristofoli, (unknown), Roberto Nonis, Pierpaolo Palestri	3
8	Deep learning acceleration based on in-memory computing 2019 ·IBM Journal of Research and Development ·Evangelos Eleftheriou, Manuel Le Gallo, S. R. Nandakumar, Christophe Piveteau, Irem Boybat, Vinay Joshi, Riduan Khaddam-Aljameh, Martino Dazzi, (unknown), Geethan Karunaratne, Benedikt Kersting, Miloš Stanisavljević, Vara Prasad Jonnalagadda, Nikolas Ioannou, (unknown), Pier Andrea Francese, Abu Sebastian	25
9	6.1 A 100Gb/s 1.1pJ/b PAM-4 RX with Dual-Mode 1-Tap PAM-4 / 3-Tap NRZ Speculative DFE in 14nm CMOS FinFET 2019 ·Alessandro Cevrero, İlter Özkaya, Pier Andrea Francese, Matthias Brändli, Christian Menolfi, Thomas Morf, Marcel Kossel, Lukas Kull, Danny Luu, Martino Dazzi, Thomas Toifl	55
10	Computational memory-based inference and training of deep neural networks 2019 ·Infoscience (Ecole Polytechnique Fédérale de Lausanne) ·Abu Sebastian, Irem Boybat, Martino Dazzi, (unknown), (unknown), Vinay Joshi, Geethan Karunaratne, Benedikt Kersting, Riduan Khaddam-Aljameh, S. R. Nandakumar, Αναστάσιος Πετρόπουλος, Christophe Piveteau, Theodore Antonakopoulos, Bipin Rajendran, Manuel Le Gallo, Evangelos Eleftheriou	11
11	Computational memory-based inference and training of deep neural networks 2019 ·Research Portal (King's College London) ·Abu Sebastian, Irem Boybat, Martino Dazzi, (unknown), (unknown), Vinay Joshi, Geethan Karunaratne, Benedikt Kersting, Riduan Khaddam-Aljameh, S. R. Nandakumar, Αναστάσιος Πετρόπουλος, Christophe Piveteau, Theodore Antonakopoulos, Bipin Rajendran, Manuel Le Gallo, Evangelos Eleftheriou	14
12	Sub-mW multi-Gbps chip-to-chip communication Links for Ultra-Low Power IoT end-nodes 2018 ·Institutional Research Information System (University of Udine) ·Martino Dazzi, Pierpaolo Palestri, Davide Rossi, (unknown), Igor Loi, (unknown), Luca Benini	5

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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