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751 results on '"Franchi A"'

1. A Readout Scheme for PCM-Based Analog In-Memory Computing With Drift Compensation Through Reference Conductance Tracking

Author

Alessio Antolini, Andrea Lico, Francesco Zavalloni, Eleonora Franchi Scarselli, Antonio Gnudi, Mattia Luigi Torres, Roberto Canegallo, and Marco Pasotti

Subjects
Analog in-memory computing (AIMC), artificial intelligence, drift compensation, matrix-vector multiplication (MVM), phase-change memory (PCM), Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4
Abstract

This article presents a readout scheme for analog in-memory computing (AIMC) based on an embedded phase-change memory (ePCM). Conductance time drift is overcome with a hardware compensation technique based on a reference cell conductance tracking (RCCT). Accuracy drop due to circuits mismatch and variability involved in the computational chain are minimized with an optimized iterative program-and-verify algorithm applied to the phase-change memory (PCM) devices. The proposed AIMC scheme is designed and manufactured in a 90-nm STMicroelectronics CMOS technology, with the aim of adding a signed multiply-and-accumulate (MAC) computation feature to a Ge-Rich GeSbTe (GST) embedded PCM array. Experimental characterizations are performed under different operating conditions and show that the mean MAC decrease in time is approximately null at room temperature and reduced by a factor of 3 after 64-h bake at $85~{^{\circ }}$ C. Based on several MAC operations, the estimated $512\times 512$ matrix-vector-multiplication (MVM) accuracy is 97.4%, whose decrease in time is less than 3% in the worst case.

Published
2024
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11. The Robust Semantic Segmentation UNCV2023 Challenge Results

Author

Yu, Xuanlong, primary, Zuo, Yi, additional, Wang, Zitao, additional, Zhang, Xiaowen, additional, Zhao, Jiaxuan, additional, Yang, Yuting, additional, Jiao, Licheng, additional, Peng, Rui, additional, Wang, Xinyi, additional, Zhang, Junpei, additional, Zhang, Kexin, additional, Liu, Fang, additional, Alcover-Couso, Roberto, additional, SanMiguel, Juan C., additional, Escudero-Viñolo, Marcos, additional, Tian, Hanlin, additional, Matsui, Kenta, additional, Wang, Tianhao, additional, Adan, Fahmy, additional, Gao, Zhitong, additional, He, Xuming, additional, Bouniot, Quentin, additional, Moghaddam, Hossein, additional, Rai, Shyam Nandan, additional, Cermelli, Fabio, additional, Masone, Carlo, additional, Pilzer, Andrea, additional, Ricci, Elisa, additional, Bursuc, Andrei, additional, Solin, Arno, additional, Trapp, Martin, additional, Li, Rui, additional, Yao, Angela, additional, Chen, Wenlong, additional, Simpson, Ivor, additional, Campbell, Neill D. F., additional, and Franchi, Gianni, additional

Published
2023
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24. An embedded PCM Peripheral Unit adding Analog MAC In-Memory Computing Feature addressing Non-linearity and Time Drift Compensation

Author

Alessio Antolini, Andrea Lico, Eleonora Franchi Scarselli, Antonio Gnudi, Luca Perilli, Mattia Luigi Torres, Marcella Carissimi, Marco Pasotti, Roberto Antonio Canegallo, IEEE European Solid-State Circuits Conference, and Alessio Antolini, Andrea Lico, Eleonora Franchi Scarselli, Antonio Gnudi, Luca Perilli, Mattia Luigi Torres, Marcella Carissimi, Marco Pasotti, Roberto Antonio Canegallo

Subjects
Analog in-memory computing (AIMC), drift compensation, multiply and accumulate operation (MAC), Phase-change memory (PCM)
Published
2022

25. One Versus All for Deep Neural Network for Uncertainty (OVNNI) Quantification

Author

Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Severine Dubuisson, and Isabelle Bloch

Subjects
General Computer Science, General Engineering, General Materials Science, Uncertainty estimation, Electrical engineering. Electronics. Nuclear engineering, DNN ensembles, one vs all classification, all vs all classification, TK1-9971
Abstract

Deep neural networks (DNNs) are powerful learning models, yet their results are not always reliable. This drawback results from the fact that modern DNNs are usually overconfident, and consequently their epistemic uncertainty cannot be straightforwardly characterized. In this work, we propose a new technique to quantify easily the epistemic uncertainty of data. This method consists in mixing the predictions of an ensemble of DNNs trained to classify One class versus All the other classes (OVA) with predictions from a standard DNN trained to perform All versus All (AVA) classification. First of all, the adjustment provided by the AVA DNN to the score of the base classifiers allows for a more fine-grained inter-class separation. Moreover, the two types of classifiers enforce mutually their detection of out-of-distribution (OOD) samples, circumventing entirely the requirement of using such samples during training. The additional cost involved by the construction of the ensemble is offset by the ease of use of our proposed strategy and by its enhanced generalization potential, as it does not bind its performance in a given context to specific OOD datasets. The extensive experiments confirm the wide applicability of our approach, and our method achieves state of the art performance in quantifying OOD data across multiple datasets and architectures while requiring little hyper-parameter tuning.

Published
2022

27. A Signal Temporal Logic Planner for Ergonomic Human–Robot Collaboration

Author

Silano, Giuseppe, Afifi, Amr, Saska, Martin, Franchi, Antonio, Robotics and Mechatronics, and Digital Society Institute

Abstract

This paper proposes a method for designing human-robot collaboration tasks and generating corresponding trajectories. The method uses high-level specifications, expressed as a STL formula, to automatically synthesize task assignments and trajectories. To illustrate the approach, we focus on a specific task: a multi-rotor aerial vehicle performing object handovers in a power line setting. The motion planner considers limitations, such as payload capacity and recharging constraints, while ensuring that the trajectories are feasible. Additionally, the method enables users to specify robot behaviors that take into account human comfort (e.g., ergonomics, preferences) while using high-level goals and constraints. The approach is validated through numerical analyzes in MATLAB and realistic Gazebo simulations using a mock-up scenario.

Published
2023

31. On Monocular Depth Estimation and Uncertainty Quantification Using Classification Approaches for Regression

Author

Yu, Xuanlong, Franchi, Gianni, and Aldea, Emanuel

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Machine Learning (cs.LG)
Abstract

Monocular depth is important in many tasks, such as 3D reconstruction and autonomous driving. Deep learning based models achieve state-of-the-art performance in this field. A set of novel approaches for estimating monocular depth consists of transforming the regression task into a classification one. However, there is a lack of detailed descriptions and comparisons for Classification Approaches for Regression (CAR) in the community and no in-depth exploration of their potential for uncertainty estimation. To this end, this paper will introduce a taxonomy and summary of CAR approaches, a new uncertainty estimation solution for CAR, and a set of experiments on depth accuracy and uncertainty quantification for CAR-based models on KITTI dataset. The experiments reflect the differences in the portability of various CAR methods on two backbones. Meanwhile, the newly proposed method for uncertainty estimation can outperform the ensembling method with only one forward propagation., 4 pages (main paper), 2 pages (supp.) Main paper is accepted at ICIP 2022

Published
2022

41. Phase-Change Memory in Neural Network Layers with Measurements-based Device Models

Author

Carmine Paolino, Alessio Antolini, Fabio Pareschi, Mauro Mangia, Riccardo Rovatti, Eleonora Franchi Scarselli, Gianluca Setti, Roberto Canegallo, Marcella Carissimi, Marco Pasotti, IEEE International Symposium on Circuits and Systems, Paolino, Carmine, Antolini, Alessio, Pareschi, Fabio, Mangia, Mauro, Rovatti, Riccardo, Scarselli, Eleonora Franchi, Setti, Gianluca, Canegallo, Roberto, Carissimi, Marcella, and Pasotti, Marco

Subjects
Phase change materials, Computational modeling, Neural networks, Phase change memory, Phase change materials, Performance evaluation, Training, Perturbation methods, Computational modeling, Neural networks, Phase change memory
Published
2022

42. Three Fundamental Paradigms for Aerial Physical Interaction Using Nonlinear Model Predictive Control

Author

Ayham Alharbat, Hanieh Esmaeeli, Davide Bicego, Abeje Mersha, Antonio Franchi, and Robotics and Mechatronics

Subjects
22/4 OA procedure
Abstract

This paper introduces and compares the three most relevant approaches in which an Aerial Physical Interaction (APhI) control can include a Nonlinear Model Predictive Control (NMPC) paradigm in its design. All these methods have the advantage of being able to cope seamlessly with input and state constraints when compared to reactive controllers, however, they substantially differ in the design of the cost function. In the NMPC impedance control the cost function includes the error from the desired impedance dynamics; in the NMPC cascaded control the cost function includes the error from a reference trajectory which is generated online by an admittance filter driven by the force measurement; and in the NMPC hybrid position/force control the cost function contains both the trajectory and direct force error. The three architectures are proposed, implemented, analysed, validated, and compared with real-time simulations of interaction tasks with different environments. The numerical investigation provides a set of insights about the performances, advantages, and dependency on the design assumptions of the three methods.

Published
2022

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