David Atienza Alonso

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With increasing power densities, runtime thermal management is becoming a necessity in today’s systems, especially so for highly integrated Multi-Processor Systems-on-Chip (MPSoCs). In this paper, we propose a neural network (NN) based approach to implemen ...

With the rising challenges in heat removal in integrated circuits (ICs), the development of thermal-aware computing architectures and run-time management systems have become indispensable to the continuation of IC design scaling. These thermal-aware design ...

This paper presents an automated real-time atrial fibrillation (AF) detection approach that relies on the observation of two characteristic irregularities of AF episodes in the electrocardiogram (ECG) signal. The results generated after the analysis of the ...

The energy efficiency of digital architectures is tightly linked to the voltage level (Vdd) at which they operate. Aggressive voltage scaling is therefore mandatory when ultra-low power processing is required. Nonetheless, the lowest admissible Vdd is ofte ...

Wireless Body Sensor Networks (WBSN) are poised to become a key enabling technology of personal systems for pervasive healthcare. Recent results have however shown that the conventional approach to their design, which consists in continuous wireless stream ...

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains ...

With the growing complexity in consumer embedded products, new tendencies forecast heterogeneous Multi-Processor Systems-On-Chip (MPSoCs) consisting of complex integrated components communicating with each other at very high-speed rates. Intercommunication ...

In the last years the process of examining large amounts of different types of data, or Big-Data, in an effort to uncover hidden patterns or unknown correlations has become a major need in our society. In this context, stream mining applications are now wi ...

Emerging massively parallel architectures such as a general-purpose processor plus many-core programmable accelerators are creating an increasing demand for novel methods to perform their architectural simulation. Most state-of-the-art simulation technolog ...

One of the greatest challenges in the design of modern wearable devices is energy efficiency. While data processing and communication have received a lot of attention from the industry and academia, leading to highly efficient microcontrollers and transmis ...

Nanoscale technology nodes bring reliability concerns back to the center stage of digital system design. A systematic classification of approaches that increase system resilience in the presence of functional hardware (HW)-induced errors is presented, deal ...

In-Memory Computing (IMC) solutions, and particularly bitline computing in SRAM, appear promising as they mitigate one of the most energy consuming aspects in computation: data movement. In this work we propose a fast (2.4Ghz for bitwise operations and 2.4 ...

Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric tele-cardiology systems. A WBSN-enabled electrocardiogram (ECG) monitor consists of wearable, miniaturized and wireless sensors able to measure and wirelessly r ...

The use of nanowire crossbars to build devices with large storage capabilities is a very promising architectural paradigm for forthcoming nanoscale memory devices. However, this new type of memory devices raises questions regarding how to test their correc ...

Technology scaling enables the design of low cost biosignal processing chips suited for emerging wireless body-area sensing applications. Energy consumption severely limits such applications and memories are becoming the energy bottleneck to achieve ultra- ...

Heat removal is one of the major challenges faced in developing the new generation of high density integrated circuits. Future design technologies strongly depend on the availability of efficient means for thermal modeling and analysis. These thermal model ...

New multimedia embedded applications are increasingly dynamic, and rely on dynamically-allocated data types (DDTs) to store their data. The optimization of DDTs for each target embedded system is a time-consuming process due to the large searching space of ...

Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epileptic seizure detection. Unfortunately, due to differences in data preparati ...

Multi-Chiplet architectures are being increasingly adopted to support the design of very large systems in a single package, facilitating the integration of heterogeneous components and improving manufacturing yield. However, chiplet-based solutions have to ...

Environmental energy is becoming a feasible alternative for many low-power systems, such as wireless sensor nodes. Designing an environmentally powered device faces several challenges: choosing the exact type of the energy harvester, the energy storage ele ...

David Atienza Alonso

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Neural Network based On-Chip Thermal Simulator

Neural Network-Based Thermal Simulation of Integrated Circuits on GPUs

Automated Real-Time Atrial Fibrillation Detection on a Wearable Wireless Sensor Platform

An Inexact Ultra-low Power Bio-signal Processing Architecture With Lightweight Error Recovery

Implementation of an automated ECG-based diagnosis for a wireless body sensor platform

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

Network-On-Chip Design and Synthesis Outlook

Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing

GPU Acceleration for Simulating Massively Parallel Many-Core Platforms

An Event-Based System for Low-Power ECG QRS Complex Detection

Classification of Resilience Techniques Against Functional Errors at Higher Abstraction Layers of Digital Systems

A Fast, Reliable and Wide-voltage-range In-memory Computing Architecture

A Real-Time Compressed Sensing-Based Personal Electrocardiogram Monitoring System

A Stochastic Perturbative Approach to Design a Defect-Aware Thresholder in the Sense Amplifier of Crossbar Memories

Approximate Compressed Sensing: Ultra-Low Power Biosignal Processing via Aggressive Voltage Scaling on a Hybrid Memory Multi-core Processor

Fast Thermal Simulation of 2D/3D Integrated Circuits Exploiting Neural Networks and GPUs

A Parallel Evolutionary Algorithm to Optimize Dynamic Data Types in Embedded Systems

Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

System-Level Exploration of In-Package Wireless Communication for Multi-Chiplet Platforms

Stochastic Modeling and Analysis for Environmentally Powered Wireless Sensor Nodes