1 code implementation • 3 Apr 2024 • Victor J. B. Jung, Alessio Burrello, Moritz Scherer, Francesco Conti, Luca Benini
Moreover, we show that our MHSA depth-first tiling scheme reduces the memory peak by up to 6. 19x, while the fused-weight attention can reduce the runtime by 1. 53x, and number of parameters by 25%.
1 code implementation • 3 Apr 2024 • Luca Benfenati, Daniele Jahier Pagliari, Luca Zanatta, Yhorman Alexander Bedoya Velez, Andrea Acquaviva, Massimo Poncino, Enrico Macii, Luca Benini, Alessio Burrello
For AD, we achieve a near-perfect 99. 9% accuracy with a monitoring time span of just 15 windows.
1 code implementation • 2 Apr 2024 • Mohamed Amine Hamdi, Giovanni Pollo, Matteo Risso, Germain Haugou, Alessio Burrello, Enrico Macii, Massimo Poncino, Sara Vinco, Daniele Jahier Pagliari
The combination of GVSoC and SystemC-AMS in a single simulation framework allows to perform a DSE that is dependent on the mutual impact between functional and extra-functional aspects.
no code implementations • 23 Feb 2024 • Matteo Risso, Francesco Daghero, Beatrice Alessandra Motetti, Daniele Jahier Pagliari, Enrico Macii, Massimo Poncino, Alessio Burrello
Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring.
no code implementations • 2 Feb 2024 • Matteo Risso, Chen Xie, Francesco Daghero, Alessio Burrello, Seyedmorteza Mollaei, Marco Castellano, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Low-resolution infrared (IR) array sensors enable people counting applications such as monitoring the occupancy of spaces and people flows while preserving privacy and minimizing energy consumption.
no code implementations • 26 Jan 2024 • Beatrice Alessandra Motetti, Luca Crupi, Mustafa Omer Mohammed Elamin Elshaigi, Matteo Risso, Daniele Jahier Pagliari, Daniele Palossi, Alessio Burrello
Sub-10cm diameter nano-drones are gaining momentum thanks to their applicability in scenarios prevented to bigger flying drones, such as in narrow environments and close to humans.
no code implementations • 29 Nov 2023 • Fabrizio Ferrandi, Serena Curzel, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Alessio Burrello, Francesco Barchi, Luca Benini, Luciano Lavagno, Teodoro Urso, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Nicola Petra, Davide De Caro, Gennaro Di Meo, Valeria Cardellini, Salvatore Filippone, Francesco Lo Presti, Francesco Silvestri, Paolo Palazzari, Stefania Perri
This survey provides a holistic review of the most influential design methodologies and EDA tools proposed in recent years to implement Deep Learning accelerators, offering the reader a wide perspective in this rapidly evolving field.
2 code implementations • 11 Oct 2023 • Alessio Burrello, Matteo Risso, Beatrice Alessandra Motetti, Enrico Macii, Luca Benini, Daniele Jahier Pagliari
The rapid proliferation of computing domains relying on Internet of Things (IoT) devices has created a pressing need for efficient and accurate deep-learning (DL) models that can run on low-power devices.
1 code implementation • 18 Jul 2023 • Daniele Jahier Pagliari, Matteo Risso, Beatrice Alessandra Motetti, Alessio Burrello
Accurate yet efficient Deep Neural Networks (DNNs) are in high demand, especially for applications that require their execution on constrained edge devices.
1 code implementation • 16 Jun 2023 • Francesco Daghero, Alessio Burrello, Enrico Macii, Paolo Montuschi, Massimo Poncino, Daniele Jahier Pagliari
With the increasing popularity of Internet of Things (IoT) devices, there is a growing need for energy-efficient Machine Learning (ML) models that can run on constrained edge nodes.
1 code implementation • 8 Jun 2023 • Matteo Risso, Alessio Burrello, Giuseppe Maria Sarda, Luca Benini, Enrico Macii, Massimo Poncino, Marian Verhelst, Daniele Jahier Pagliari
The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a diverse set of hardware accelerators.
no code implementations • 8 Jun 2023 • Alessio Burrello, Matteo Risso, Noemi Tomasello, Yukai Chen, Luca Benini, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
In this work, we propose a collaborative inference approach that uses both a smartwatch and a connected smartphone to maximize the performance of heart rate (HR) tracking while also maximizing the smartwatch's battery life.
no code implementations • 3 Mar 2023 • Elia Cereda, Luca Crupi, Matteo Risso, Alessio Burrello, Luca Benini, Alessandro Giusti, Daniele Jahier Pagliari, Daniele Palossi
In this work, we leverage a novel neural architecture search (NAS) technique to automatically identify several Pareto-optimal convolutional neural networks (CNNs) for a visual pose estimation task.
1 code implementation • 24 Jan 2023 • Matteo Risso, Alessio Burrello, Francesco Conti, Lorenzo Lamberti, Yukai Chen, Luca Benini, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the structure of Deep Learning (DL) models for complex tasks such as Image Classification or Object Detection.
no code implementations • 14 Oct 2022 • Panagiotis Kasnesis, Lazaros Toumanidis, Alessio Burrello, Christos Chatzigeorgiou, Charalampos Z. Patrikakis
Nowadays, Hearth Rate (HR) monitoring is a key feature of almost all wrist-worn devices exploiting photoplethysmography (PPG) sensors.
no code implementations • 2 Sep 2022 • Francesco Daghero, Alessio Burrello, Chen Xie, Marco Castellano, Luca Gandolfi, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
With experiments on four datasets, and targeting an ultra-low-power RISC-V MCU, we show that (i) We are able to obtain a rich set of Pareto-optimal CNNs for HAR, spanning more than 1 order of magnitude in terms of memory, latency and energy consumption; (ii) Thanks to adaptive inference, we can derive >20 runtime operating modes starting from a single CNN, differing by up to 10% in classification scores and by more than 3x in inference complexity, with a limited memory overhead; (iii) on three of the four benchmarks, we outperform all previous deep learning methods, reducing the memory occupation by more than 100x.
1 code implementation • 17 Jun 2022 • Matteo Risso, Alessio Burrello, Luca Benini, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Quantization is widely employed in both cloud and edge systems to reduce the memory occupation, latency, and energy consumption of deep neural networks.
1 code implementation • 1 Jun 2022 • Matteo Risso, Alessio Burrello, Luca Benini, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
When deployed on a commercial edge device, the STM NUCLEO-H743ZI2, our networks span a range of 2. 18x in energy consumption and 4. 04% in accuracy for the same memory constraint, and reduce energy by up to 2. 2x with negligible accuracy drop with respect to the baseline.
no code implementations • 27 May 2022 • Francesco Daghero, Alessio Burrello, Chen Xie, Luca Benini, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
The accuracy of a RF often increases with the number of internal weak learners (decision trees), but at the cost of a proportional increase in inference latency and energy consumption.
1 code implementation • 25 May 2022 • Francesco Daghero, Chen Xie, Daniele Jahier Pagliari, Alessio Burrello, Marco Castellano, Luca Gandolfi, Andrea Calimera, Enrico Macii, Massimo Poncino
In this work, we propose a novel implementation of HAR based on deep neural networks, and precisely on Binary Neural Networks (BNNs), targeting low-power general purpose processors with a RISC-V instruction set.
no code implementations • 7 Apr 2022 • Francesco Daghero, Alessio Burrello, Daniele Jahier Pagliari, Luca Benini, Enrico Macii, Massimo Poncino
Energy-efficient machine learning models that can run directly on edge devices are of great interest in IoT applications, as they can reduce network pressure and response latency, and improve privacy.
1 code implementation • 28 Mar 2022 • Matteo Risso, Alessio Burrello, Daniele Jahier Pagliari, Francesco Conti, Lorenzo Lamberti, Enrico Macii, Luca Benini, Massimo Poncino
Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing tasks.
no code implementations • 28 Mar 2022 • Matteo Risso, Alessio Burrello, Daniele Jahier Pagliari, Simone Benatti, Enrico Macii, Luca Benini, Massimo Poncino
A wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU to enable non-invasive and comfortable monitoring, but ensuring robust PPG-based heart-rate monitoring in the presence of motion artifacts is still an open challenge.
1 code implementation • 24 Mar 2022 • Alessio Burrello, Daniele Jahier Pagliari, Matteo Risso, Simone Benatti, Enrico Macii, Luca Benini, Massimo Poncino
Our most accurate quantized network achieves 4. 41 Beats Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of 47. 65 mJ and a memory footprint of 412 kB.
no code implementations • 24 Mar 2022 • Alessio Burrello, Alberto Dequino, Daniele Jahier Pagliari, Francesco Conti, Marcello Zanghieri, Enrico Macii, Luca Benini, Massimo Poncino
Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series analysis.
no code implementations • 24 Mar 2022 • Alessio Burrello, Francesco Bianco Morghet, Moritz Scherer, Simone Benatti, Luca Benini, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms.
1 code implementation • 4 Mar 2022 • Amirhossein Moallemi, Alessio Burrello, Davide Brunelli, Luca Benini
Modern real-time Structural Health Monitoring systems can generate a considerable amount of information that must be processed and evaluated for detecting early anomalies and generating prompt warnings and alarms about the civil infrastructure conditions.
no code implementations • 1 Mar 2022 • Alessio Burrello, Daniele Jahier Pagliari, Pierangelo Maria Rapa, Matilde Semilia, Matteo Risso, Tommaso Polonelli, Massimo Poncino, Luca Benini, Simone Benatti
Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart-rate (HR) monitoring, suitable for compact wrist-worn devices.
1 code implementation • 25 Mar 2021 • Thorir Mar Ingolfsson, Xiaying Wang, Michael Hersche, Alessio Burrello, Lukas Cavigelli, Luca Benini
With 9. 91 GMAC/s/W, it is 23. 0 times more energy-efficient and 46. 85 times faster than an implementation on the ARM Cortex M4F (0. 43 GMAC/s/W).
1 code implementation • 17 Aug 2020 • Alessio Burrello, Angelo Garofalo, Nazareno Bruschi, Giuseppe Tagliavini, Davide Rossi, Francesco Conti
In this work, we propose DORY (Deployment Oriented to memoRY) - an automatic tool to deploy DNNs on low cost MCUs with typically less than 1MB of on-chip SRAM memory.
no code implementations • 6 Sep 2018 • Alessio Burrello, Kaspar Schindler, Luca Benini, Abbas Rahimi
This paper presents an efficient binarized algorithm for both learning and classification of human epileptic seizures from intracranial electroencephalography (iEEG).