David Atienza Alonso

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Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can ...

This paper presents a novel method for classification of multi-lead electrocardiogram (ECG) signals. The feature extraction is based on the random projection (RP) concept for dimensionality reduction. Furthermore, the classification is performed by a neuro ...

With the growing complexity in consumer embedded products and the improvements in process technology, Multi-Processor System-On-Chip (MPSoC) architectures have become widespread. These new systems are very complex to design as they must execute multiple co ...

Nowadays, Java is used in all types of embedded devices. For these memory-constrained systems, the automatic dynamicmemorymanager (Garbage Collector or GC) has been always a key factor in terms of the Java Virtual Machine (JVM) performance. Moreover, in cu ...

Heat removal and power density distribution delivery have become two major reliability concerns in 3D stacked technology. In this paper, we propose a thermal-driven 3D floorplanner. Our contributions include: (1) a novel multi-objective formulation to cons ...

The quest for technologies with superior device characteristics has showcased Carbon Nanotube Field Effect Transistors (CNFETs) into limelight. Among the several design aspects necessary for today’s grail in CNFET technology, achieving functional immunity ...

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic o ...

In the context of wearable medical systems, resources are scarce while performance requirements are high. Traditional sampling strategies create large amounts of data, which hinders the device's battery lifetime. However, energy savings are possible when r ...

Accurate power and performance figures are critical to assess the effective design of possible sensor node architectures in Body Area Networks (BANs) since they operate on limited energy storage. Therefore, accurate power models and simulation tools that c ...

Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life. Despite advances in machine learning and IoT, small, non-stigmatizing wearable devices for continuous monito ...

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, w ...

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

Atrial Fibrillation (AF) is a type of cardiac arrhythmia that significantly increases the risk of stroke and heart failure. In general, in the case of patients affected by AF, their electrocardiogram (ECG) shows a typical pattern of irregular RR intervals ...

Compressed Sensing (CS) is a new acquisition- compression paradigm for low-complexity energy-aware sensing and compression. By merging both sampling and compression, CS is very promising to develop practical ultra-low power read- out systems for wireless b ...

Hybrid caches consisting of both SRAM and emerging Non-Volatile Random Access Memory (eNVRAM) bitcells increase cache capacity and reduce power consumption by taking advantage of eNVRAM's small area footprint and low leakage energy. However, they also inhe ...

Microchannel liquid cooling has been proposed since the late 2000s as a viable enabler for 3D integration of microprocessors to continue scaling of computing power in the face of increasingly reduced returns from CMOS technology scaling. Thermal and electr ...

The rapid expansion of online-based services requires novel energy and performance efficient architectures to meet power and latency constraints. Fast architectural exploration has become a key enabler in the proposal of architectural innovation. In this p ...

Die-temperature control to avoid hotspots is increasingly critical in Multiprocessor System-on-Chip (MPSoCs) for stream computing. In this context, thermal balancing policies based on task migration are a promising approach to re-distribute power dissipati ...

This paper introduces an inexact, but ultra-low power, computing architecture devoted to the embedded analysis of bio-signals. The platform operates at extremely low voltage supply levels to minimize energy consumption. In this scenario, the reliability of ...

Modern system-on-chips are evolving towards complex and heterogeneous platforms with general-purpose processors coupled with massively parallel manycore accelerator fabrics (e.g. embedded GPUs). Platform developers are looking for efficient full-system sim ...

David Atienza Alonso

Chattez avec Graph Search

Overflow-free Compute Memories for Edge AI Acceleration

A Multi-Lead Ecg Classification Based On Random Projection Features

Architectural Exploration of MPSoC Designs Based on an FPGA Emulation Framework

Memory Power Optimization of Java-Based Embedded Systems Exploiting Garbage Collection Information

3D Thermal-Aware Floorplanner for Many-Core Single-Chip Systems

Design of Compact Imperfection-Immune CNFET Layouts for Standard-Cell-Based Logic Synthesis

Cognitive workload monitoring in virtual reality based rescue missions with drones

Event-Triggered Sensing for High-Quality and Low-Power Cardiovascular Monitoring Systems

OS-Based Sensor Node Platform and Energy Estimation Model for Health-Care Wireless Sensor Networks

Importance of methodological choices in data manipulation for validating epileptic seizure detection models

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

Hierarchical Cardiac-Rhythm Classification Based on Electrocardiogram Morphology

Power-Efficient Joint Compressed Sensing of Multi-Lead ECG Signals

A Hybrid Cache HW/SW Stack for Optimizing Neural Network Runtime, Power and Endurance

ICCAD 2015 Contest in 3D Interlayer Cooling Optimized Network

Gem5-X: A Gem5-Based System Level Simulation Framework to Optimize Many-Core Platforms

Thermal Balancing Policy for Multiprocessor Stream Computing Platforms

Inexact-Aware Architecture Design for Ultra-Low Power Bio-Signal Analysis

Full System Simulation of Many-Core Heterogeneous SoCs using GPU and QEMU Semihosting