ECONAI-CMS v1
Published:
Reprogrammable AI accelerator for CMS calorimeter with radiation hardening
Posts by Collection
hardware
CryoAI v1
Published:
Ultra-low-power ML accelerator with custom cryogenic memories and RISC-V
Sprocket v1 - xray imaging
Published:
Radiation-tolerant ML accelerator for on-edge anomaly detection in particle physics
In-Pixel AI v1 - Photon Science Detector
Published:
ML-based compression accelerator for photon science and particle detection
CryoAI v2 - Quantum Control ML Accelerator
Published:
Low-power ML accelerator for quantum readout and control in cryogenic environment
CITC2 - Cryogenic Mixed-Signal Readout ASIC
Published:
Digital controller and readout for mixed-signal cryogenic ASIC with 200MHz timing
CMS28v2 – AI In-Pixel Readout Chip for HL-LHC
Published:
Full digital logic and layout of a radiation-hard pixel ASIC in 28nm CMOS with integrated neural network classifiers
patents
Automatic breakouts detection and characterization method from reservoir well images
Published:
Brazilian patent application for automatic detection and characterization of breakouts in reservoir well images.
Recommended citation: Albuquerque, M. P., Valentin, M. B., Faria, E. L., Correia, M. D., Albuquerque, M. P., Dias, L. O., & Bom, C. R. (2019). Automatic breakouts detection and characterization method from reservoir well images. BR102018009637A2.
Analysis based on sensitivity characteristic data and hardware measurement data
Published:
Patent on analysis methods using sensitivity characteristic data and hardware measurement data
Recommended citation: Valentin, M., Braun, A., Keller, I., & Amin, C. (2023). Analysis based on sensitivity characteristic data and hardware measurement data. U.S. Patent Office, 23PA168US01.
Method of automatic characterization and removal of pad artifacts in ultrasonic images of wells
Published:
Patent application for automatic characterization and removal of pad artifacts in ultrasonic images of reservoir wells.
Recommended citation: Albuquerque, M. P., Rodrigues, B. B., Dias, L., Faria, E. L., Albuquerque, M. P., Bom, C. R., Valentim, M. B., & Oliveira, A. P. A. (2024). Method of automatic characterization and removal of pad artifacts in ultrasonic images of wells. US20240029209A2.
publications
Desarrollo del sistema electrónico de control de navegación de un barco remolcador a escala
Published in Universitat Politècnica de Catalunya. Escola Universitària d'Enginyeria …, 2014
Desarrollo del sistema electrónico de control de navegación de un barco remolcador a escala.
Recommended citation: Manuel Blanco Valentín (2014). "Desarrollo del sistema electrónico de control de navegación de un barco remolcador a escala." Universitat Politècnica de Catalunya. Escola Universitària d'Enginyeria ….
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Rock Texture Classification Using Spectral Analysis And Genetically Optimized Texture Features
Published in Computer Vision and Pattern Recognition (CVPR), 2016
Rock Texture Classification Using Spectral Analysis And Genetically Optimized Texture Features.
Recommended citation: Manuel Blanco Valentin, Clecio Roque De Bom, Marcio Portes de Albuquerque, Marcelo Portes de Albuquerque, Elisangela Faria, Maury Duarte Correia, Rodrigo Surmas (2016). "Rock Texture Classification Using Spectral Analysis And Genetically Optimized Texture Features." Computer Vision and Pattern Recognition (CVPR).
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Texture classification based on spectral analysis and haralick features
Published in Notas Técnicas, 2016
Texture classification based on spectral analysis and haralick features.
Recommended citation: Manuel Blanco Valentin, Clécio Roque de Bom, Márcio P de Albuquerque, Marcelo P de Albuquerque, Elisângela L Faria, Maury D Correia (2016). "Texture classification based on spectral analysis and haralick features." Notas Técnicas. 6(1).
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On a method for Rock Classification using Textural Features and Genetic Optimization
Published in Notas Técnicas CBPF-NT-002/10 Abril 2017, 2017
On a method for Rock Classification using Textural Features and Genetic Optimization.
Recommended citation: Manuel Blanco Valentin, Clecio Roque De Bom, Marcio Portes de Albuquerque, Marcelo Portes de Albuquerque, Elisangela Faria, Maury Duarte Correia, Rodrigo Surmas (2016). "On a method for Rock Classification using Textural Features and Genetic Optimization." Notas Técnicas CBPF-NT-002/10 Abril 2017.
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Ultrasound image acquisition as a didactic tool
Published in Revista Brasileira de Ensino de Física, 2018
Ultrasound image acquisition as a didactic tool.
Recommended citation: M Valentín, C de Bom, M de Albuquerque, ECorreia M Faria (2018). "Ultrasound image acquisition as a didactic tool." Revista Brasileira de Ensino de Física. 40(2).
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Porous medium permeability estimation for well imagery and characterization using complex resistivity spectra
Published in Notas Técnicas CBPF-NT-002/18 fevereiro 2018, 2018
Porous medium permeability estimation for well imagery and characterization using complex resistivity spectra.
Recommended citation: Manuel Blanco Valentın, Márcio P de Albuquerque, Marcelo P de Albuquerque, Elisângela L Faria, Yann Le Guével, Clécio Roque de Bom, Maury D Correia (2018). "Notas Técnicas CBPF-NT-002/18 fevereiro 2018."
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Deep learning methods on geological reservoir borehole log images and applications
Published in Centro Brasileiro de Pesquisas Físicas, 2018
Deep learning methods on geological reservoir borehole log images and applications.
Recommended citation: Blanco-Valentin, Manuel (2018). "Deep learning methods on geological reservoir borehole log images and applications." Centro Brasileiro de Pesquisas Físicas.
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Estimation of permeability and effective porosity logs using deep autoencoders in borehole image logs from the Brazilian pre-salt carbonate
Published in Journal of Petroleum Science and Engineering, 2018
Estimation of permeability and effective porosity logs using deep autoencoders in borehole image logs from the Brazilian pre-salt carbonate.
Recommended citation: Manuel Blanco Valentin, Clecio R Bom, André Luiz Martins Compan, Maury Duarte Correia, Candida Menezes de Jesus, Anelise de Lima Souza, Marcio P de Albuquerque, Marcelo P de Albuquerque, Elisangela L Faria (2018). "Estimation of permeability and effective porosity logs using deep autoencoders in borehole image logs from the Brazilian pre-salt carbonate." Journal of Petroleum Science and Engineering. 170, 315–330.
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The strong gravitational lens finding challenge
Published in Astronomy & Astrophysics, 2019
Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images, and deriving scienti…
Recommended citation: R Benton Metcalf, M Meneghetti, Camille Avestruz, Fabio Bellagamba, Clécio R Bom, Emmanuel Bertin, Rémi Cabanac, F Courbin, Andrew Davies, Etienne Decencière, others (2019). "The strong gravitational lens finding challenge." Astronomy & Astrophysics. 625, A119.
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A deep residual convolutional neural network for automatic lithological facies identification in Brazilian pre-salt oilfield wellbore image logs
Published in Journal of Petroleum Science and Engineering, 2019
A deep residual convolutional neural network for automatic lithological facies identification in Brazilian pre-salt oilfield wellbore image logs.
Recommended citation: Manuel Blanco Valentín, Clécio R Bom, Juliana M Coelho, Maury Duarte Correia, Márcio P De Albuquerque, Marcelo P de Albuquerque, Elisângela L Faria (2019). "A deep residual convolutional neural network for automatic lithological facies identification in Brazilian pre-salt oilfield wellbore image logs." Journal of Petroleum Science and Engineering. 179, 474–503.
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Deep learning in wide-field surveys: Fast analysis of strong lenses in ground-based cosmic experiments
Published in arXiv preprint arXiv:1911.06341, 2019
Searches and analyses of strong gravitational lenses are challenging due to the rarity and image complexity of these astronomical objects. Next-generation surveys (both ground- and space-based) will provide more opportunities to derive science from these objects, but only if the…
Recommended citation: Clecio Bom, Jason Poh, Brian Nord, Manuel Blanco-Valentin, Luciana Dias (2019). "Deep learning in wide-field surveys: Fast analysis of strong lenses in ground-based cosmic experiments." arXiv preprint arXiv:1911.06341.
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Automatic detection of fractures and breakouts patterns in acoustic borehole image logs using fast-region convolutional neural networks
Published in Journal of Petroleum Science and Engineering, 2020
Automatic detection of fractures and breakouts patterns in acoustic borehole image logs using fast-region convolutional neural networks.
Recommended citation: Luciana Olivia Dias, Clécio R Bom, Elisangela L Faria, Manuel Blanco Valentín, Maury Duarte Correia, Márcio P De Albuquerque, Marcelo P De Albuquerque, Juliana M Coelho (2020). "Automatic detection of fractures and breakouts patterns in acoustic borehole image logs using fast-region convolutional neural networks." Journal of Petroleum Science and Engineering. 191, 107099.
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A Cryogenic Readout IC with 100 KSPS in-Pixel ADC for Skipper CCD-in-CMOS Sensors [Poster]
Published in Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States), 2021
A Cryogenic Readout IC with 100 KSPS in-Pixel ADC for Skipper CCD-in-CMOS Sensors [Poster].
Recommended citation: A Quinn, MB Valentin, T Zimmerman, D Braga, S Li, S Memik, F Fahim (2021). "A Cryogenic Readout IC with 100 KSPS in-Pixel ADC for Skipper CCD-in-CMOS Sensors [Poster]." Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States).
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hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices
Published in arXiv preprint arXiv:2103.05579, 2021
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design…
Recommended citation: Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P Carloni, Giuseppe Di Guglielmo, Philip Harris, Jeffrey Krupa, others (2021). "hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices." arXiv preprint arXiv:2103.05579.
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Bayesian deep networks for absolute permeability and porosity uncertainty prediction from image borehole logs from Brazilian carbonate reservoirs
Published in Journal of Petroleum Science and Engineering, 2021
Bayesian deep networks for absolute permeability and porosity uncertainty prediction from image borehole logs from Brazilian carbonate reservoirs.
Recommended citation: Clecio R Bom, Manuel Blanco Valentin, Bernardo MO Fraga, Jorge Campos, Bernardo Coutinho, Luciana O Dias, Elisangela L Faria, Marcio P de Albuquerque, Marcelo P de Albuquerque, Maury Duarte Correia (2021). "Bayesian deep networks for absolute permeability and porosity uncertainty prediction from image borehole logs from Brazilian carbonate reservoirs." Journal of Petroleum Science and Engineering. 201, 108361.
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A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
Published in IEEE Transactions on Nuclear Science, 2021
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC.
Recommended citation: Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig, Manuel Blanco Valentin, Javier Duarte, Cristian Gingu, Philip Harris, James Hirschauer, Martin Kwok, Vladimir Loncar, others (2021). "A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC." IEEE Transactions on Nuclear Science. 68(8), 2179–2186.
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Developing a victorious strategy to the second strong gravitational lensing data challenge
Published in Monthly Notices of the Royal Astronomical Society, 2022
ABSTRACT Strong lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with deep learning have become a popular approach due to these astronomical objects’ rarity…
Recommended citation: CR Bom, Bernardo Machado de Oliveira Fraga, Luciana Olivia Dias, Patrick Schubert, M Blanco Valentin, Cristina Furlanetto, Martín Makler, K Teles, M Portes de Albuquerque, R Benton Metcalf (2022). "Developing a victorious strategy to the second strong gravitational lensing data challenge." Monthly Notices of the Royal Astronomical Society. 515(4), 5121–5134.
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Neural network accelerator for universal quantum control
Published in APS March Meeting Abstracts, 2023
Neural network accelerator for universal quantum control.
Recommended citation: A Baris Ozguler, Giuseppe Di Guglielmo, Manuel Blanco Valentín, David Xu, Nhan Tran, Gabriel Perdue, Luca Carloni, Farah Fahim (2023). "Neural network accelerator for universal quantum control." APS March Meeting Abstracts. 2023, F71–006.
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Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties
Published in arXiv preprint arXiv:2312.11676, 2023
The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine angular information of the c…
Recommended citation: Jennet Dickinson, Rachel Kovach-Fuentes, Lindsey Gray, Morris Swartz, Giuseppe Di Guglielmo, Alice Bean, Doug Berry, Manuel Blanco Valentin, Karri DiPetrillo, Farah Fahim, others (2023). "Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties." arXiv preprint arXiv:2312.11676.
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A cryogenic readout ic with 100 ksps in-pixel adc for skipper ccd-in-cmos sensors
Published in 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 2023
A cryogenic readout ic with 100 ksps in-pixel adc for skipper ccd-in-cmos sensors.
Recommended citation: Adam Quinn, Manuel B Valentin, Thomas Zimmerman, Davide Braga, Seda Memik, Farah Fahim (2023). "A cryogenic readout ic with 100 ksps in-pixel adc for skipper ccd-in-cmos sensors." 2023 IEEE International Symposium on Circuits and Systems (ISCAS). 1–5.
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In-pixel AI for lossy data compression at source for X-ray detectors
Published in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2023
In-pixel AI for lossy data compression at source for X-ray detectors.
Recommended citation: Manuel B Valentin, Giuseppe Di Guglielmo, Danny Noonan, Priyanka Dilip, Panpan Huang, Adam Quinn, Thomas Zimmerman, Davide Braga, Seda Ogrenci, Chris Jacobsen, others (2023). "In-pixel AI for lossy data compression at source for X-ray detectors." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 1057, 168665.
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Smart Pixels: Algorithm design and hardware testing for a 28m ROIC for future pixel trackers
Published in APS April Meeting Abstracts, 2024
Smart Pixels: Algorithm design and hardware testing for a 28m ROIC for future pixel trackers.
Recommended citation: Carissa Kumar, Emily Pan, Karri Dipetrillo, Anthony Badea, Jennet Dickinson, Jieun Yoo, Morris Swartz, Giuseppe Di Guglielmo, Alice Bean, Douglas Berry, others (2024). "Smart Pixels: Algorithm design and hardware testing for a 28m ROIC for future pixel trackers." APS April Meeting Abstracts. 2024, F14–005.
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Radiation-Hard Smart-Pixel Detector ASIC ReadOut with Digital AI in 28nm
Published in Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States), 2024
Radiation-Hard Smart-Pixel Detector ASIC ReadOut with Digital AI in 28nm.
Recommended citation: Benjamin Parpillon, Anthony Badea, Chinar Syal, Corrinne Mills, Douglas Berry, Farah Fahim, Giuseppe Di Guglielmo, Jennet Dickinson, Jieun Yoo, Jim Hirschauer, others (2024). "Radiation-Hard Smart-Pixel Detector ASIC ReadOut with Digital AI in 28nm." Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States).
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Smart Pixels: In-pixel AI for on-sensor data filtering
Published in 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 2024
Smart Pixels: In-pixel AI for on-sensor data filtering.
Recommended citation: Benjamin Parpillon, Chinar Syal, Jieun Yoo, M Swartz, G Di Guglielmo, A Bean, D Berry, M Blanco Valentin, K DiPetrillo, A Badea, others (2024). "Smart Pixels: In-pixel AI for on-sensor data filtering." 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). 1–2.
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An Ultra-Low-Voltage, 16-Channel Current DAC ASIC in 22nm FDSOI for cryogenic SQUID Biasing
Published in SMT 2025, 2025
An Ultra-Low-Voltage, 16-Channel Current DAC ASIC in 22nm FDSOI for cryogenic SQUID Biasing.
Recommended citation: Davide Braga, Suyash P Tripathi, Farah Fahim, Manuel B Valentin, Paul Rubinov, Robert F McDermott, John M Martinis, Lou Dal Monte (2025). "An Ultra-Low-Voltage, 16-Channel Current DAC ASIC in 22nm FDSOI for cryogenic SQUID Biasing." SMT 2025.
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hls4ml: A flexible, open-source platform for deep learning acceleration on reconfigurable hardware
Published in ACM Transactions on Reconfigurable Technology and Systems, 2025
We present hls4ml , a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate…
Recommended citation: Jan-Frederik Schulte, Benjamin Ramhorst, Chang Sun, Jovan Mitrevski, Nicolò Ghielmetti, Enrico Lupi, Dimitrios Danopoulos, Vladimir Loncar, Javier Duarte, David Burnette, others (2025). "hls4ml: A flexible, open-source platform for deep learning acceleration on reconfigurable hardware." ACM Transactions on Reconfigurable Technology and Systems.
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Toward Reconfigurable In-Pixel Computing: A Fault-Tolerant Design Flow for Machine Learning Accelerators
Published in 2025 IEEE 33rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2025
Toward Reconfigurable In-Pixel Computing: A Fault-Tolerant Design Flow for Machine Learning Accelerators.
Recommended citation: Houxuan Guo, Manuel Blanco Valentín, Xiuyuan He, Seda Ogrenci (2025). "Toward Reconfigurable In-Pixel Computing: A Fault-Tolerant Design Flow for Machine Learning Accelerators." 2025 IEEE 33rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). 261–267.
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software
hls4ml-trainable
Published:
Trainable extensions for hardware-aware machine-learning workflows
🧶 Nodus: A lightweight and reusable job manager.
Published:
Lightweight and reusable job manager for complex workflows
📜 Pergamos: Dynamic HTML Reporting for Python
Published:
Dynamic HTML reporting for Python applications
talks
📟 A Cryogenic Readout IC with 100 KSPS in-Pixel ADC for Skipper CCD-in-CMOS Sensors - Fermilab 2021
Published:
Technical presentation on a cryogenic readout IC with in-pixel ADC for Skipper CCD-in-CMOS sensors.
🤖 CryoAI – Prototyping cryogenic chips for machine learning at 22nm
Published:
Talk on CryoAI, a 22nm cryogenic chip prototype for machine-learning workloads.
Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning
Published:
Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning.
Recommended citation: Jieun Yoo, Jennet Dickinson, Morris Swartz, Giuseppe Di Guglielmo, Alice Bean, Douglas Berry, Manuel Blanco Valentin, Karri DiPetrillo, Farah Fahim, Lindsey Gray, others (2024). "Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning." Machine Learning: Science and Technology. 5(3), 035047.
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Radiation-Hard Smart-Pixel Detector ASIC ReadOut with Digital AI in 28nm
Published:
Radiation-Hard Smart-Pixel Detector ASIC ReadOut with Digital AI in 28nm.
Recommended citation: Benjamin Parpillon, Anthony Badea, Chinar Syal, Corrinne Mills, Douglas Berry, Farah Fahim, Giuseppe Di Guglielmo, Jennet Dickinson, Jieun Yoo, Jim Hirschauer, others (2024). "Radiation-Hard Smart-Pixel Detector ASIC ReadOut with Digital AI in 28nm." Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States).
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🤖 ENABOL: Enabling Neural Backpropagation On-chip Learning for Edge AI Systems
Published:
Talk on ENABOL for on-chip learning in edge AI systems.
🧠 The (\mathcal{NRCSTK}) neuron: Survival-drivel learning via spectral tuning under metabolic competition - Cosyne 2026
Published:
Poster on neuromorphic learning systems and ASIC design for neuroscience applications.
teaching
ASIC Design & Verification Lecturer (Invited Instructor)
Short course / Workshop, CERN INFIERI School (Universidad Autónoma de Madrid), 2021
Invited instructor for a 5-day lecture/lab series covering ASIC design, verification, P&R, DRC/LVS, and signoff.