Daniele Jahier Pagliari

Daniele Jahier Pagliari
Politecnico di Torino | polito · DAUIN - Department of Control and Computer Engineering

PhD

About

75
Publications
10,784
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369
Citations
Introduction
Assistant Professor (RTD-B) in the EDA Group of Politecnico di Torino. Interested in Embedded Systems, Embedded Machine Learning, EDA and Low-Power Design.

Publications

Publications (75)
Chapter
Random Forests (RFs) are popular Machine Learning models for edge computing, due to their lightweight nature and high accuracy on several common tasks. Large RFs however, still have significant energy costs, a serious concern for battery-operated ultra-low-power devices. Following the adaptive (or dynamic) inference paradigm, we introduce a hardwar...
Article
Full-text available
Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on embedded devices, from smartphones to ultra low-power sensors. Due to the high computational complexity of deep learning models, most embedded HAR systems are based on simple and not-so-accurate classic machine learning algorithms. This work bridges the gap...
Article
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Background Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time...
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Stratifying prognosis following coronary bifurcation percutaneous coronary intervention (PCI) is an unmet clinical need that may be fulfilled through the adoption of machine learning (ML) algorithms to refine outcome predictions. We sought to develop an ML-based risk stratification model built on clinical, anatomical, and procedural features to pre...
Preprint
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Quantization is widely employed in both cloud and edge systems to reduce the memory occupation, latency, and energy consumption of deep neural networks. In particular, mixed-precision quantization, i.e., the use of different bit-widths for different portions of the network, has been shown to provide excellent efficiency gains with limited accuracy...
Preprint
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Human Activity Recognition (HAR) has become an increasingly popular task for embedded devices such as smartwatches. Most HAR systems for ultra-low power devices are based on classic Machine Learning (ML) models, whereas Deep Learning (DL), although reaching state-of-the-art accuracy, is less popular due to its high energy consumption, which poses a...
Preprint
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Neural Architecture Search (NAS) is increasingly popular to automatically explore the accuracy versus computational complexity trade-off of Deep Learning (DL) architectures. When targeting tiny edge devices, the main challenge for DL deployment is matching the tight memory constraints, hence most NAS algorithms consider model size as the complexity...
Preprint
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Random Forests (RFs) are widely used Machine Learning models in low-power embedded devices, due to their hardware friendly operation and high accuracy on practically relevant tasks. 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 ene...
Article
Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart rate (HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, motion artifacts (MAs) severely impact the monitoring accuracy, causing high variability in the skin-to-sensor interface. Several data fusion techniques have been introduced to cope with this...
Preprint
Full-text available
Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adap...
Preprint
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Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables for social distance monitoring in indoor spaces, permitting the recognition of basic shapes, without revealing the personal details of individuals. In this work, we demonstrate that an accurate dete...
Preprint
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Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices. Albeit beneficial for other tasks, CI has never been applied to the sequence- to-sequence mapping problem at the heart of Neural Machine Translation (NMT). In this work, we address the specific...
Preprint
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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. An effective way to obtain energy-efficiency with small accuracy drops is to sequentially execute a set of increasingly complex models, early-stoppin...
Preprint
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Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing tasks. One key feature of TCNs is time-dilated convolution, whose optimization requires extensive experimentation. We propose an automatic dilation optimizer, which tackles the problem as a weight pruning on the time-axis, and learns dilation factor...
Preprint
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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. Recent state-of-the-art algorithms combine PPG and inertial signals to mitigate the effect of motion arti...
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Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series analysis. We introduce an automated exploration approach and a library of optimized kernels to map TCNs on Parallel Ultra-Low Power (PULP) microcontrollers. Our approach minimizes latency and energy by exploiting a layer tiling optimizer to jointly...
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Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms. Gesture recognition exploiting surface electromyographic (sEMG) signals is one of the most promising approaches, given that sEMG signal acquisition is non-invasive and is directly related to muscle contraction. However, the a...
Preprint
Full-text available
Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by movements of the subject's arm affect the performance of PPG-based HR tracking. This is typically addressed coupling the PPG signal with acceleration measurements from an inertial s...
Article
Traffic Load Estimation (TLE) is increasingly adopted in public road infrastructures to regulate the access and limit heavy vehicles circulation. Standard approaches to TLE are based either on installing dedicated sensors such as intelligent cameras or infrared sensors or using existing smartphone sensors. However, both approaches have severe limit...
Preprint
Full-text available
Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart-rate (HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, Motion Artifacts (MAs) severely impact the monitoring accuracy, causing high variability in the skin-to-sensor interface. Several data fusion techniques have been introduced to cope with this...
Article
Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) affect the performance of PPG-based HR tracking. This is typically addressed coupling the PPG signal with acceleration measurements from an inertial sensor. Unfortunately, most standard appro...
Article
Full-text available
A well-known system-level strategy to reduce the energy consumption of microprocessors or microcontrollers is to organize the scheduling of the executed tasks so that it is aware of the main battery non-idealities. In the IoT domain, devices rely on simpler microcontrollers; workloads are less rich and, batteries are typically sized to guarantee li...
Conference Paper
Full-text available
Human Activity Recognition (HAR) is a relevant inference task in many mobile applications. State-of-the-art HAR at the edge is typically achieved with lightweight machine learning models such as decision trees and Random Forests (RFs), whereas deep learning is less common due to its high computational complexity. In this work, we propose a novel im...
Chapter
In modern data centers, storage system failures are major contributors to downtimes and maintenance costs. Predicting these failures by collecting measurements from disks and analyzing them with machine learning techniques can effectively reduce their impact, enabling timely maintenance. While there is a vast literature on this subject, most approa...
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In recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor environments. The new Industry 4.0 model allows smart factories to become very advanced IT industries, generating an ever-increasing amount of valuable data. As a consequence, the n...
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Smart sensors present in ubiquitous Internet of Things (IoT) devices often obtain high energy efficiency by carefully tuning how the sensing, the analog to digital (A/D) conversion and the digital serial transmission are implemented. Such tuning involves approximations, i.e. alterations of the sensed signals that can positively affect energy consum...
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The success of deep learning comes at the cost of very high computational complexity. Consequently, Internet of Things (IoT) edge nodes typically offload deep learning tasks to powerful cloud servers, an inherently inefficient solution. In fact, transmitting raw data to the cloud through wireless links incurs long latencies and high energy consumpt...
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The aging of rechargeable batteries, with its associated replacement costs, is one of the main issues limiting the diffusion of electric vehicles (EVs) as the future transportation infrastructure. An effective way to mitigate battery aging is to act on its charge cycles, more controllable than discharge ones, implementing so-called battery-aware ch...
Article
The design of photovoltaic (PV) installations mostly relies on rule-of-thumb criteria and on gross estimates of the shading patterns, and the few optimized approaches are generally focused on the problem of identifying the most suitable surfaces (e.g., roofs) in a larger geographic area (e.g., city or district). This work proposes a framework to ad...
Conference Paper
Full-text available
Given the computational complexity of Recurrent Neural Networks (RNNs) inference, IoT, and mobile devices typically offload this task to the cloud. However, the execution time and energy consumption of RNN inference strongly depend on the length of the processed input. Therefore, considering also communication costs, it may be more convenient to pr...
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Sequence-to-sequence deep neural networks have become the state of the art for a variety of machine learning applications, ranging from neural machine translation (NMT) to speech recognition. Many mobile and Internet of Things (IoT) applications would benefit from the ability of performing sequence-to-sequence inference directly in embedded devices...
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Modern Cyber-Physical Electrical Energy Systems (CPEES) are characterized by wider adoption of sustainable energy sources and by an increased attention to optimization, with the goal of reducing pollution and wastes. This imposes a need for instruments supporting the design flow, to simulate and validate the behavior of system components and to app...
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Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoo...
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In recent years, designers are trying to move part of the computing tasks involved in Internet of Things applications from the cloud to the edge. This imposes increasing performance demands on edge nodes, which usually clash with their limited energy budget. An effective workaround is to leverage hardware capable of varying its computational precis...
Conference Paper
Full-text available
Smart meters communicate to the utility provider fine-grain information about a user's energy consumption, which could be used to infer the user's habits and pose thus a critical privacy risk. State-of-the-art solutions try to obfuscate the readings of a meter either by using a large re-chargeable battery to filter the trace or by adding random noi...
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The range of operations of electric vehicles (EVs) is a critical aspect that may affect the user's attitude toward them. For manned EVs, range anxiety is still perceived as a major issue and recent surveys have shown that one-third of potential European users are deterred by this problem when considering the move to an EV. A similar consideration a...
Conference Paper
Full-text available
Recurrent Neural Networks (RNNs) are state-of-the-art models for many machine learning tasks, such as language modeling and machine translation. Executing the inference phase of a RNN directly in edge nodes, rather than in the cloud, would provide benefits in terms of energy consumption, latency and network bandwidth, provided that models can be ma...
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Organic Light Emitting Diode (OLED) is rapidly emerging as the mainstream mobile display technology. This is posing new challenges on the design of energy-saving solutions for OLED displays, specifically intended for interactive devices such as smartphones, smartwatches and tablets. To this date, the standard solution is brightness scaling. However...
Article
Smart systems are characterized by the integration in a single device of multi-domain subsystems of different technological domains, namely, analog, digital, discrete and power devices, MEMS, and power sources. Such challenges, emerging from the heterogeneous nature of the whole system, combined with the traditional challenges of digital design, di...
Conference Paper
Full-text available
Deep learning models have reached state of the art performance in many machine learning tasks. Benefits in terms of energy, bandwidth, latency, etc., can be obtained by evaluating these models directly within Internet of Things end nodes, rather than in the cloud. This calls for implementations of deep learning tasks that can run in resource limite...
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Full-text available
Organic Light Emitting Diode (OLED) display panels are becoming increasingly popular especially in mobile devices; one of the key characteristics of these panels is that their power consumption strongly depends on the displayed image. In this paper we propose LAPSE, a new methodology to concurrently reduce the energy consumed by an OLED display and...
Conference Paper
Full-text available
Lifetime maximization is a key challenge in battery-powered multi-sensor devices. Battery-aware power management strategies combine task scheduling with dynamic voltage scaling (DVS), accounting for the fact that the power drawn by the device is different from that provided by the battery due to its many non-idealities. However, state-of-the-art te...
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Energy-quality scalable systems are a promising solution to cope with the small energy budgets and high processing demands of mobile and IoT applications. These systems leverage the error resilience of applications to obtain high energy efficiency, at the expense of tolerable reductions in the output quality. Hardware datapath operators able to rec...