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1. Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices

Author

Fra, Vittorio, Leto, Benedetto, Pignata, Andrea, Macii, Enrico, and Urgese, Gianvito

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Neuromorphic models take inspiration from the human brain by adopting bio-plausible neuron models to build alternatives to traditional Machine Learning (ML) and Deep Learning (DL) solutions. The scarce availability of dedicated hardware able to actualize the emulation of brain-inspired computation, which is otherwise only simulated, yet still hinders the wide adoption of neuromorphic computing for edge devices and embedded systems. With this premise, we adopt the perspective of neuromorphic computing for conventional hardware and we present the L2MU, a natively neuromorphic Legendre Memory Unit (LMU) which entirely relies on Leaky Integrate-and-Fire (LIF) neurons. Specifically, the original recurrent architecture of LMU has been redesigned by modelling every constituent element with neural populations made of LIF or Current-Based (CuBa) LIF neurons. To couple neuromorphic computing and off-the-shelf edge devices, we equipped the L2MU with an input module for the conversion of real values into spikes, which makes it an encoding-free implementation of a Recurrent Spiking Neural Network (RSNN) able to directly work with raw sensor signals on non-dedicated hardware. As a use case to validate our network, we selected the task of Human Activity Recognition (HAR). We benchmarked our L2MU on smartwatch signals from hand-oriented activities, deploying it on three different commercial edge devices in compressed versions too. The reported results remark the possibility of considering neuromorphic models not only in an exclusive relationship with dedicated hardware but also as a suitable choice to work with common sensors and devices., Comment: Paper accepted for the 33rd International Conference on Artificial Neural Networks (ICANN 2024)

Published
2024

2. ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers

Author

Chen, Zihao, Xiao, Zhili, Akl, Mahmoud, Leugring, Johannes, Olajide, Omowuyi, Malik, Adil, Dennler, Nik, Harper, Chad, Bose, Subhankar, Gonzalez, Hector A., Eshraghian, Jason, Pignari, Riccardo, Urgese, Gianvito, Andreou, Andreas G., Shankar, Sadasivan, Mayr, Christian, Cauwenberghs, Gert, and Chakrabartty, Shantanu

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using an annealing process that is governed by the physics of quantum mechanical tunneling using Fowler-Nordheim (FN). The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing (SA) dynamics onto a network of integrate-and-fire (IF) neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer which replicates the optimal escape mechanism and convergence of SA, particularly at low temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved various benchmark MAX-CUT combinatorial optimization problems. Across multiple runs, NeuroSA consistently generates solutions that approach the state-of-the-art level with high accuracy (greater than 99%), and without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform., Comment: 36 pages, 8 figures

Published
2024

3. Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing

Author

Pedersen, Jens E., Abreu, Steven, Jobst, Matthias, Lenz, Gregor, Fra, Vittorio, Bauer, Felix C., Muir, Dylan R., Zhou, Peng, Vogginger, Bernhard, Heckel, Kade, Urgese, Gianvito, Shankar, Sadasivan, Stewart, Terrence C., Sheik, Sadique, and Eshraghian, Jason K.

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available at neuroir.org, Comment: NIR is available at https://neuroir.org

Published
2023
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4. WaLiN-GUI: a graphical and auditory tool for neuron-based encoding

Author

Müller-Cleve, Simon F., Quintana, Fernando M., Fra, Vittorio, Galindo, Pedro L., Perez-Peña, Fernando, Urgese, Gianvito, and Bartolozzi, Chiara

Subjects
Computer Science - Neural and Evolutionary Computing, Quantitative Biology - Neurons and Cognition
Abstract

Neuromorphic computing relies on spike-based, energy-efficient communication, inherently implying the need for conversion between real-valued (sensory) data and binary, sparse spiking representation. This is usually accomplished using the real valued data as current input to a spiking neuron model, and tuning the neuron's parameters to match a desired, often biologically inspired behaviour. We developed a tool, the WaLiN-GUI, that supports the investigation of neuron models and parameter combinations to identify suitable configurations for neuron-based encoding of sample-based data into spike trains. Due to the generalized LIF model implemented by default, next to the LIF and Izhikevich neuron models, many spiking behaviors can be investigated out of the box, thus offering the possibility of tuning biologically plausible responses to the input data. The GUI is provided open source and with documentation, being easy to extend with further neuron models and personalize with data analysis functions., Comment: 4 pages, 1 figure

Published
2023

6. Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware

Author

Muller-Cleve, Simon F, Fra, Vittorio, Khacef, Lyes, Pequeno-Zurro, Alejandro, Klepatsch, Daniel, Forno, Evelina, Ivanovich, Diego G, Rastogi, Shavika, Urgese, Gianvito, Zenke, Friedemann, and Bartolozzi, Chiara

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Computer Science - Robotics
Abstract

Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstanding accuracies in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic applications is a representative example where real-time processing and energy efficiency are required. Following a brain-inspired computing approach, we propose a new benchmark for spatio-temporal tactile pattern recognition at the edge through Braille letter reading. We recorded a new Braille letters dataset based on the capacitive tactile sensors of the iCub robot's fingertip. We then investigated the importance of spatial and temporal information as well as the impact of event-based encoding on spike-based computation. Afterward, we trained and compared feedforward and recurrent Spiking Neural Networks (SNNs) offline using Backpropagation Through Time (BPTT) with surrogate gradients, then we deployed them on the Intel Loihi neuromorphic chip for fast and efficient inference. We compared our approach to standard classifiers, in particular to the Long Short-Term Memory (LSTM) deployed on the embedded NVIDIA Jetson GPU, in terms of classification accuracy, power, energy consumption, and delay. Our results show that the LSTM reaches ~97% of accuracy, outperforming the recurrent SNN by ~17% when using continuous frame-based data instead of event-based inputs. However, the recurrent SNN on Loihi with event-based inputs is ~500 times more energy-efficient than the LSTM on Jetson, requiring a total power of only ~30 mW. This work proposes a new benchmark for tactile sensing and highlights the challenges and opportunities of event-based encoding, neuromorphic hardware, and spike-based computing for spatio-temporal pattern recognition at the edge.

Published
2022

7. RISC-V Processor Technologies for Aerospace Applications in the ISOLDE Project

Author

Fornaciari, William, Reghenzani, Federico, Agosta, Giovanni, Zoni, Davide, Galimberti, Andrea, Conti, Francesco, Tortorella, Yvan, Parisi, Emanuele, Barchi, Francesco, Bartolini, Andrea, Acquaviva, Andrea, Gregori, Daniele, Cognetta, Salvatore, Ciancarelli, Carlo, Leboffe, Antonio, Serri, Paolo, Burrello, Alessio, Pagliari, Daniele Jahier, Urgese, Gianvito, Martina, Maurizio, Masera, Guido, Di Carlo, Rosario, Sciarappa, Antonio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Silvano, Cristina, editor, Pilato, Christian, editor, and Reichenbach, Marc, editor

Published
2023
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10. Predicting the Oncogenic Potential of Gene Fusions Using Convolutional Neural Networks

Author

Lovino, Marta, Urgese, Gianvito, Macii, Enrico, di Cataldo, Santa, Ficarra, Elisa, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Raposo, Maria, editor, Ribeiro, Paulo, editor, Sério, Susana, editor, Staiano, Antonino, editor, and Ciaramella, Angelo, editor

Published
2020
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12. NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Author

Yik, Jason, Van den Berghe, Korneel, den Blanken, Douwe, Bouhadjar, Younes, Fabre, Maxime, Hueber, Paul, Kleyko, Denis, Pacik-Nelson, Noah, Sun, Pao-Sheng Vincent, Tang, Guangzhi, Wang, Shenqi, Zhou, Biyan, Hasan Ahmed, Soikat, Vathakkattil Joseph, George, Leto, Benedetto, Micheli, Aurora, Mishra, Anurag Kumar, Lenz, Gregor, Sun, Tao, Ahmed, Zergham, Akl, Mahmoud, Anderson, Brian, Andreou, Andreas G., Bartolozzi, Chiara, Basu, Arindam, Bogdan, Petrut, Bohte, Sander, Buckley, Sonia, Cauwenberghs, Gert, Chicca, Elisabetta, Corradi, Federico, de Croon, Guido, Danielescu, Andreea, Daram, Anurag, Davies, Mike, Demirag, Yigit, Eshraghian, Jason, Fischer, Tobias, Forest, Jeremy, Fra, Vittorio, Furber, Steve, Furlong, P. Michael, Gilpin, William, Gilra, Aditya, Gonzalez, Hector A., Indiveri, Giacomo, Joshi, Siddharth, Karia, Vedant, Khacef, Lyes, Knight, James C., Kriener, Laura, Kubendran, Rajkumar, Kudithipudi, Dhireesha, Liu, Yao-Hong, Liu, Shih-Chii, Ma, Haoyuan, Manohar, Rajit, Margarit-Taulé, Josep Maria, Mayr, Christian, Michmizos, Konstantinos, Muir, Dylan, Neftci, Emre, Nowotny, Thomas, Ottati, Fabrizio, Ozcelikkale, Ayca, Panda, Priyadarshini, Park, Jongkil, Payvand, Melika, Pehle, Christian, Petrovici, Mihai A., Pierro, Alessandro, Posch, Christoph, Renner, Alpha, Sandamirskaya, Yulia, Schaefer, Clemens J.S., van Schaik, André, Schemmel, Johannes, Schmidgall, Samuel, Schuman, Catherine, Seo, Jae-sun, Sheik, Sadique, Bam Shrestha, Sumit, Sifalakis, Manolis, Sironi, Amos, Stewart, Matthew, Stewart, Kenneth, Stewart, Terrence C., Stratmann, Philipp, Timcheck, Jonathan, Tömen, Nergis, Urgese, Gianvito, Verhelst, Marian, Vineyard, Craig M., Vogginger, Bernhard, Yousefzadeh, Amirreza, Tuz Zohora, Fatima, Frenkel, Charlotte, Janapa Reddi, Vijay, Yik, Jason, Van den Berghe, Korneel, den Blanken, Douwe, Bouhadjar, Younes, Fabre, Maxime, Hueber, Paul, Kleyko, Denis, Pacik-Nelson, Noah, Sun, Pao-Sheng Vincent, Tang, Guangzhi, Wang, Shenqi, Zhou, Biyan, Hasan Ahmed, Soikat, Vathakkattil Joseph, George, Leto, Benedetto, Micheli, Aurora, Mishra, Anurag Kumar, Lenz, Gregor, Sun, Tao, Ahmed, Zergham, Akl, Mahmoud, Anderson, Brian, Andreou, Andreas G., Bartolozzi, Chiara, Basu, Arindam, Bogdan, Petrut, Bohte, Sander, Buckley, Sonia, Cauwenberghs, Gert, Chicca, Elisabetta, Corradi, Federico, de Croon, Guido, Danielescu, Andreea, Daram, Anurag, Davies, Mike, Demirag, Yigit, Eshraghian, Jason, Fischer, Tobias, Forest, Jeremy, Fra, Vittorio, Furber, Steve, Furlong, P. Michael, Gilpin, William, Gilra, Aditya, Gonzalez, Hector A., Indiveri, Giacomo, Joshi, Siddharth, Karia, Vedant, Khacef, Lyes, Knight, James C., Kriener, Laura, Kubendran, Rajkumar, Kudithipudi, Dhireesha, Liu, Yao-Hong, Liu, Shih-Chii, Ma, Haoyuan, Manohar, Rajit, Margarit-Taulé, Josep Maria, Mayr, Christian, Michmizos, Konstantinos, Muir, Dylan, Neftci, Emre, Nowotny, Thomas, Ottati, Fabrizio, Ozcelikkale, Ayca, Panda, Priyadarshini, Park, Jongkil, Payvand, Melika, Pehle, Christian, Petrovici, Mihai A., Pierro, Alessandro, Posch, Christoph, Renner, Alpha, Sandamirskaya, Yulia, Schaefer, Clemens J.S., van Schaik, André, Schemmel, Johannes, Schmidgall, Samuel, Schuman, Catherine, Seo, Jae-sun, Sheik, Sadique, Bam Shrestha, Sumit, Sifalakis, Manolis, Sironi, Amos, Stewart, Matthew, Stewart, Kenneth, Stewart, Terrence C., Stratmann, Philipp, Timcheck, Jonathan, Tömen, Nergis, Urgese, Gianvito, Verhelst, Marian, Vineyard, Craig M., Vogginger, Bernhard, Yousefzadeh, Amirreza, Tuz Zohora, Fatima, Frenkel, Charlotte, and Janapa Reddi, Vijay

Abstract

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community.

Published
2024

13. Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing

Author

Pedersen, Jens, Abreu, Steven, Jobst, Matthias, Lenz, Gregor, Fra, Vittorio, Bauer, Felix Christian, Muir, Dylan Richard, Zhou, Peng, Vogginger, Bernhard, Heckel, Kade, Urgese, Gianvito, Shankar, Sadasivan, Stewart, Terrence C., Sheik, Sadique, Eshraghian, Jason K., Pedersen, Jens, Abreu, Steven, Jobst, Matthias, Lenz, Gregor, Fra, Vittorio, Bauer, Felix Christian, Muir, Dylan Richard, Zhou, Peng, Vogginger, Bernhard, Heckel, Kade, Urgese, Gianvito, Shankar, Sadasivan, Stewart, Terrence C., Sheik, Sadique, and Eshraghian, Jason K.

Abstract

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available at neuroir.org, QC 20240926

Published
2024
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14. Mapping Spiking Neural Networks on Multi-core Neuromorphic Platforms: Problem Formulation and Performance Analysis

Author

Barchi, Francesco, Urgese, Gianvito, Macii, Enrico, Acquaviva, Andrea, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Pras, Aiko, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Furbach, Ulrich, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Bombieri, Nicola, editor, Pravadelli, Graziano, editor, Fujita, Masahiro, editor, and Austin, Todd, editor

Published
2019
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15. Heterogeneity and evolution of DNA mutation rates in microsatellite stable colorectal cancer

Author

Grassi, Elena, primary, Vurchio, Valentina, additional, Cresswell, George David, additional, Catalano, Irene, additional, Lupo, Barbara, additional, Sassi, Francesco, additional, Galimi, Francesco, additional, Borgato, Sofia, additional, Ferri, Martina, additional, Viviani, Marco, additional, Pompei, Simone, additional, Urgese, Gianvito, additional, Chen, Bingjie, additional, Zanella, Eugenia Rosalinda, additional, Cottino, Francesca, additional, Bardelli, Alberto, additional, Cosentino Lagormarsino, Marco, additional, Sottoriva, Andrea, additional, Trusolino, Livio, additional, and Bertotti, Andrea, additional

Published
2024
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24. Editorial: Powering the next-generation IoT applications: new tools and emerging technologies for the development of Neuromorphic System of Systems

Author

Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Sevilla. TEP108: Robótica y Tecnología de Computadores, Spanish grant with support from the European Regional Development Fund MINDROB PID2019-105556GB-C33/AEI/10.13039/501100011033, Spanish grant with support from the European Regional Development Fund SMALL PCI2019-111841-2/AEI/10.1309/501100011033, Urgese, Gianvito, Ríos-Navarro, Antonio, Linares Barranco, Alejandro, Stewart, Terrence C., Michmizos, Konstantinos, Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Sevilla. TEP108: Robótica y Tecnología de Computadores, Spanish grant with support from the European Regional Development Fund MINDROB PID2019-105556GB-C33/AEI/10.13039/501100011033, Spanish grant with support from the European Regional Development Fund SMALL PCI2019-111841-2/AEI/10.1309/501100011033, Urgese, Gianvito, Ríos-Navarro, Antonio, Linares Barranco, Alejandro, Stewart, Terrence C., and Michmizos, Konstantinos

Abstract

The brain, this 3-pound mass of tissue that can easily be held in one's palm, has an inherent computational complexity that has always inspired efforts to endorse machines with some of its remarkable characteristics. Ironically, the brain computes in its own way, compared to analog or digital computers, despite sharing key concepts with both. It employs analog computation but digital communication, through spikes, both of which improve robustness to noise. This unique combination defines a new computational paradigm that we have just started to explore. The reasons why neuromorphic systems are one of the fastest growing applications are not purely scientific, but mainly technological. For 50 years, the principle guiding computations has been Moore's Law, a macroscopic observation that we will always find ways to engineer faster, smaller and cheaper chips. But there are several reasons why Moore's Law is no longer able to keep up. First, physics: As we downsize transistors close to the atomic scale, it becomes difficult to regulate electron flow. Electrons do not necessarily adhere to Newtonian physics and may pass through the transistor barriers, a phenomenon called quantum tunneling. This makes our computer architectures inefficient. Second, we have long accepted the existence of a trade-off between computing faster and consuming less power, but this has never been a problem until we started approaching the physical limits of fabricating transistors. And the final nail in Moore's Law coffin is put by deep learning. Our computational needs are now orders of magnitude higher than what our systems can deliver. The von-Neumann paradigm is already having more than its fair share of inefficiency—to match its brilliance. And the reason is simple. Computers have been designed with feasibility, not efficiency, at their center. And nowhere are the effects of a bad design more imminent—or the opportunity for an alternative design more compelling – than in emerging technologie

Published
2023

25. NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Author

Yik, Jason, Berghe, Korneel Van den, Blanken, Douwe den, Bouhadjar, Younes, Fabre, Maxime, Hueber, Paul, Kleyko, Denis, Pacik-Nelson, Noah, Sun, Pao-Sheng Vincent, Tang, Guangzhi, Wang, Shenqi, Zhou, Biyan, Ahmed, Soikat Hasan, Joseph, George Vathakkattil, Leto, Benedetto, Micheli, Aurora, Mishra, Anurag Kumar, Lenz, Gregor, Sun, Tao, Ahmed, Zergham, Akl, Mahmoud, Anderson, Brian, Andreou, Andreas G., Bartolozzi, Chiara, Basu, Arindam, Bogdan, Petrut, Bohte, Sander, Buckley, Sonia, Cauwenberghs, Gert, Chicca, Elisabetta, Corradi, Federico, de Croon, Guido, Danielescu, Andreea, Daram, Anurag, Davies, Mike, Demirag, Yigit, Eshraghian, Jason, Fischer, Tobias, Forest, Jeremy, Fra, Vittorio, Furber, Steve, Furlong, P. Michael, Gilpin, William, Gilra, Aditya, Gonzalez, Hector A., Indiveri, Giacomo, Joshi, Siddharth, Karia, Vedant, Khacef, Lyes, Knight, James C., Kriener, Laura, Kubendran, Rajkumar, Kudithipudi, Dhireesha, Liu, Yao-Hong, Liu, Shih-Chii, Ma, Haoyuan, Manohar, Rajit, Margarit-Taulé, Josep Maria, Mayr, Christian, Michmizos, Konstantinos, Muir, Dylan, Neftci, Emre, Nowotny, Thomas, Ottati, Fabrizio, Ozcelikkale, Ayca, Panda, Priyadarshini, Park, Jongkil, Payvand, Melika, Pehle, Christian, Petrovici, Mihai A., Pierro, Alessandro, Posch, Christoph, Renner, Alpha, Sandamirskaya, Yulia, Schaefer, Clemens JS, van Schaik, André, Schemmel, Johannes, Schmidgall, Samuel, Schuman, Catherine, Seo, Jae-sun, Sheik, Sadique, Shrestha, Sumit Bam, Sifalakis, Manolis, Sironi, Amos, Stewart, Matthew, Stewart, Kenneth, Stewart, Terrence C., Stratmann, Philipp, Timcheck, Jonathan, Tömen, Nergis, Urgese, Gianvito, Verhelst, Marian, Vineyard, Craig M., Vogginger, Bernhard, Yousefzadeh, Amirreza, Zohora, Fatima Tuz, Frenkel, Charlotte, Reddi, Vijay Janapa, Yik, Jason, Berghe, Korneel Van den, Blanken, Douwe den, Bouhadjar, Younes, Fabre, Maxime, Hueber, Paul, Kleyko, Denis, Pacik-Nelson, Noah, Sun, Pao-Sheng Vincent, Tang, Guangzhi, Wang, Shenqi, Zhou, Biyan, Ahmed, Soikat Hasan, Joseph, George Vathakkattil, Leto, Benedetto, Micheli, Aurora, Mishra, Anurag Kumar, Lenz, Gregor, Sun, Tao, Ahmed, Zergham, Akl, Mahmoud, Anderson, Brian, Andreou, Andreas G., Bartolozzi, Chiara, Basu, Arindam, Bogdan, Petrut, Bohte, Sander, Buckley, Sonia, Cauwenberghs, Gert, Chicca, Elisabetta, Corradi, Federico, de Croon, Guido, Danielescu, Andreea, Daram, Anurag, Davies, Mike, Demirag, Yigit, Eshraghian, Jason, Fischer, Tobias, Forest, Jeremy, Fra, Vittorio, Furber, Steve, Furlong, P. Michael, Gilpin, William, Gilra, Aditya, Gonzalez, Hector A., Indiveri, Giacomo, Joshi, Siddharth, Karia, Vedant, Khacef, Lyes, Knight, James C., Kriener, Laura, Kubendran, Rajkumar, Kudithipudi, Dhireesha, Liu, Yao-Hong, Liu, Shih-Chii, Ma, Haoyuan, Manohar, Rajit, Margarit-Taulé, Josep Maria, Mayr, Christian, Michmizos, Konstantinos, Muir, Dylan, Neftci, Emre, Nowotny, Thomas, Ottati, Fabrizio, Ozcelikkale, Ayca, Panda, Priyadarshini, Park, Jongkil, Payvand, Melika, Pehle, Christian, Petrovici, Mihai A., Pierro, Alessandro, Posch, Christoph, Renner, Alpha, Sandamirskaya, Yulia, Schaefer, Clemens JS, van Schaik, André, Schemmel, Johannes, Schmidgall, Samuel, Schuman, Catherine, Seo, Jae-sun, Sheik, Sadique, Shrestha, Sumit Bam, Sifalakis, Manolis, Sironi, Amos, Stewart, Matthew, Stewart, Kenneth, Stewart, Terrence C., Stratmann, Philipp, Timcheck, Jonathan, Tömen, Nergis, Urgese, Gianvito, Verhelst, Marian, Vineyard, Craig M., Vogginger, Bernhard, Yousefzadeh, Amirreza, Zohora, Fatima Tuz, Frenkel, Charlotte, and Reddi, Vijay Janapa

Abstract

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community., Comment: Updated from whitepaper to full perspective article preprint

Published
2023

26. NanoMagnet Logic: An Architectural Level Overview

Author

Vacca, Marco, Graziano, Mariagrazia, Wang, Juanchi, Cairo, Fabrizio, Causapruno, Giovanni, Urgese, Gianvito, Biroli, Andrea, Zamboni, Maurizio, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Anderson, Neal G., editor, and Bhanja, Sanjukta, editor

Published
2014
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30. MirGeneDB 2.1 : toward a complete sampling of all major animal phyla

Author

Fromm, Bastian, Høye, Eirik, Domanska, Diana, Zhong, Xiangfu, Aparicio-Puerta, Ernesto, Ovchinnikov, Vladimir, Umu, Sinan U., Chabot, Peter J., Kang, Wenjing, Aslanzadeh, Morteza, Tarbier, Marcel, Mármol-Sánchez, Emilio, Urgese, Gianvito, Johansen, Morten, Hovig, Eivind, Hackenberg, Michael, Friedländer, Marc R., Peterson, Kevin J., Fromm, Bastian, Høye, Eirik, Domanska, Diana, Zhong, Xiangfu, Aparicio-Puerta, Ernesto, Ovchinnikov, Vladimir, Umu, Sinan U., Chabot, Peter J., Kang, Wenjing, Aslanzadeh, Morteza, Tarbier, Marcel, Mármol-Sánchez, Emilio, Urgese, Gianvito, Johansen, Morten, Hovig, Eivind, Hackenberg, Michael, Friedländer, Marc R., and Peterson, Kevin J.

Abstract

We describe an update of MirGeneDB, the manually curated microRNA gene database. Adhering to uniform and consistent criteria for microRNA annotation and nomenclature, we substantially expanded MirGeneDB with 30 additional species representing previously missing metazoan phyla such as sponges, jellyfish, rotifers and flatworms. MirGeneDB 2.1 now consists of 75 species spanning over ∼800 million years of animal evolution, and contains a total number of 16 670 microRNAs from 1549 families. Over 6000 microRNAs were added in this update using ∼550 datasets with ∼7.5 billion sequencing reads. By adding new phylogenetically important species, especially those relevant for the study of whole genome duplication events, and through updating evolutionary nodes of origin for many families and genes, we were able to substantially refine our nomenclature system. All changes are traceable in the specifically developed MirGeneDB version tracker. The performance of read-pages is improved and microRNA expression matrices for all tissues and species are now also downloadable. Altogether, this update represents a significant step toward a complete sampling of all major metazoan phyla, and a widely needed foundation for comparative microRNA genomics and transcriptomics studies.

Published
2022
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31. LaRA 2: parallel and vectorized program for sequence���structure alignment of RNA sequences

Author

Winkler, Jörg, Urgese, Gianvito, Ficarra, Elisa, and Reinert, Knut

Subjects
FOS: Computer and information sciences, Parallel computing, Base Sequence, Sequence Analysis, RNA, QH301-705.5, Bioinformatics, Computer applications to medicine. Medical informatics, R858-859.7, 500 Naturwissenschaften und Mathematik::570 Biowissenschaften, Biologie::570 Biowissenschaften, Biologie, RNA secondary structure, Integer linear program, Structural alignment, Algorithms, RNA, Humans, Nucleic Acid Conformation, Sequence Alignment, Software, Biology (General), 000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung, Informatik, Sequence Analysis
Abstract

Background The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson–Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and infer their possible functions, the structural alignment of RNAs is an essential task. This task demands a lot of computational resources, especially for aligning many long sequences, and it therefore requires efficient algorithms that utilize modern hardware when available. A subset of the secondary structures contains overlapping interactions (called pseudoknots), which add additional complexity to the problem and are often ignored in available software. Results We present the SeqAn-based software LaRA 2 that is significantly faster than comparable software for accurate pairwise and multiple alignments of structured RNA sequences. In contrast to other programs our approach can handle arbitrary pseudoknots. As an improved re-implementation of the LaRA tool for structural alignments, LaRA 2 uses multi-threading and vectorization for parallel execution and a new heuristic for computing a lower boundary of the solution. Our algorithmic improvements yield a program that is up to 130 times faster than the previous version. Conclusions With LaRA 2 we provide a tool to analyse large sets of RNA secondary structures in relatively short time, based on structural alignment. The produced alignments can be used to derive structural motifs for the search in genomic databases.

Published
2022
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36. MirGeneDB 2.1: toward a complete sampling of all major animal phyla

Author

Fromm, Bastian, primary, Høye, Eirik, additional, Domanska, Diana, additional, Zhong, Xiangfu, additional, Aparicio-Puerta, Ernesto, additional, Ovchinnikov, Vladimir, additional, Umu, Sinan U, additional, Chabot, Peter J, additional, Kang, Wenjing, additional, Aslanzadeh, Morteza, additional, Tarbier, Marcel, additional, Mármol-Sánchez, Emilio, additional, Urgese, Gianvito, additional, Johansen, Morten, additional, Hovig, Eivind, additional, Hackenberg, Michael, additional, Friedländer, Marc R, additional, and Peterson, Kevin J, additional

Published
2021
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37. NanoMagnet Logic: An Architectural Level Overview

Author

Vacca, Marco, primary, Graziano, Mariagrazia, additional, Wang, Juanchi, additional, Cairo, Fabrizio, additional, Causapruno, Giovanni, additional, Urgese, Gianvito, additional, Biroli, Andrea, additional, and Zamboni, Maurizio, additional

Published
2014
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42. Unification of miRNA and isomiR research : the mirGFF3 format and the mirtop API

Author

Desvignes, Thomas, Loher, Phillipe, Eilbeck, Karen, Ma, Jeffery, Urgese, Gianvito, Fromm, Bastian, Sydes, Jason, Aparicio-Puerta, Ernesto, Barrera, Victor, Espin, Roderic, Thibord, Florian, Bofill-De Ros, Xavier, Londin, Eric, Telonis, Aristeidis G., Ficarra, Elisa, Friedländer, Marc R., Postlethwait, John H., Rigoutsos, Isidore, Hackenberg, Michael, Vlachos, Ioannis S., Halushka, Marc K., Pantano, Lorena, Desvignes, Thomas, Loher, Phillipe, Eilbeck, Karen, Ma, Jeffery, Urgese, Gianvito, Fromm, Bastian, Sydes, Jason, Aparicio-Puerta, Ernesto, Barrera, Victor, Espin, Roderic, Thibord, Florian, Bofill-De Ros, Xavier, Londin, Eric, Telonis, Aristeidis G., Ficarra, Elisa, Friedländer, Marc R., Postlethwait, John H., Rigoutsos, Isidore, Hackenberg, Michael, Vlachos, Ioannis S., Halushka, Marc K., and Pantano, Lorena

Abstract

Motivation: MicroRNAs (miRNAs) are small RNA molecules (similar to 22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods. Results: To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification.

Published
2020
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48. Single-cell DNA Sequencing Data: a Pipeline for Multi-Sample Analysis

Author

Montemurro, Marilisa, Elena, Grassi, Urgese, Gianvito, Parisi, Emanuele, Carmelo Gabriele Pizzino, Andrea, Bertotti, and Ficarra, Elisa

Subjects
evolution, tumor heterogeneity, pipeline, single-cell sequencing, pipeline, evolution, phylogenetic tree, tumor heterogeneity, phylogenetic tree, single-cell sequencing
Published
2019

50. Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API

Author

Desvignes, Thomas, primary, Loher, Phillipe, additional, Eilbeck, Karen, additional, Ma, Jeffery, additional, Urgese, Gianvito, additional, Fromm, Bastian, additional, Sydes, Jason, additional, Aparicio-Puerta, Ernesto, additional, Barrera, Victor, additional, Espín, Roderic, additional, Thibord, Florian, additional, Bofill-De Ros, Xavier, additional, Londin, Eric, additional, Telonis, Aristeidis G, additional, Ficarra, Elisa, additional, Friedländer, Marc R, additional, Postlethwait, John H, additional, Rigoutsos, Isidore, additional, Hackenberg, Michael, additional, Vlachos, Ioannis S, additional, Halushka, Marc K, additional, and Pantano, Lorena, additional

Published
2019
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