David Atienza Alonso

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Recently, efficiently deploying deep learning solutions on the edge has received increasing attention. New platforms are emerging to support the increasing demand for flexibility and high performance. In this work, we explore the efficient mapping of convo ...

Current Systems-On-Chip (SoC) execute applications that demand extensive parallel processing. Networks-On-Chip (NoC) provide a structured way of realizing interconnections on silicon, and obviate the limitations of bus-based solution. NoCs can have regular ...

A smart wearable (SW) for collecting data on an integrity of a joint of a wearer, the wearable including a first frame (1) configured to be attached in proximity of the joint of the wearer, the frame (1) including an elastic sensor frame (3) having an open ...

Advances in manufacturing technologies are enabling the development of 3D multi-processor ICs, but heat in 3D stacking can lead to degraded performance due to higher power density, which derives from the placement of computational units and storage compone ...

A random access memory having a memory array having a plurality of local memory groups, each local memory group including a plurality of bitcells arranged in a bitcell column, a pair of local bitlines operatively connected to the plurality of bitcells, a p ...

This paper presents the set of simulation means used to develop the concept of N2C2 (neural network compute cube) based on a vertical transistor technology platform. On the basis of state-of-the-art junctionless nanowire transistors (JLNT), TCAD simulation ...

A large number of algorithms for multidimensional signals processing and scientific computation come in the form of iterative stencil loops (ISLs), whose data dependencies span across multiple iterations. Because of their complex inner structure, automatic ...

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. One of its most relevant applications is the acquisition and anal ...

In this work we present a multi-path routing strategy that guarantees in-order packet delivery for Networks on Chips (NoCs). We present a design methodology that uses the routing strategy to optimally spread the traffic in the NoC to minimize the network b ...

A frequency scaling governor is critical for the performance management of cloud servers, as it enhances energy efficiency and helps to control operational temperatures, thereby ensuring system reliability. However, our in-depth analysis of the application ...

Nowadays, Deep Convolutional Neural Networks (DCNNs) play a significant role in many application domains, such as, computer vision, medical imaging, and image processing. Nonetheless, designing a DCNN, able to defeat the state of the art, is a manual, chal ...

Chip designers place on-chip thermal sensors to measure local temperatures, thus preventing thermal runaway situations in many-core processing architectures. However, the quality of the thermal reconstruction is directly dependent on the number of placed s ...

Wearable devices performing advanced bio-signal analysis algorithms will foster a revolution in healthcare provision of chronic cardiac diseases. In this context, energy efficiency is of paramount importance, as long-term operations must be performed withi ...

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

Energy-harvesting sensor nodes (EHSNs) have stringent low-energy consumption requirements, but they need to concurrently execute several types of tasks (processing, sensing, actuation, etc). Furthermore, no accurate models exist to predict the energy harve ...

Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. The COUGHVID dataset provide ...

Edge-AI applications necessitate the joint application of hardware acceleration and software optimization to meet energy and area constraints. However, the co-design of these systems is hindered by the lack of integration options for novel hardware prototy ...

New portable consumer embedded devices must execute multimedia applications (e.g., 3D games, video players and signal processing software, etc.) that demand extensive memory accesses and memory usage at a low energy consumption. Moreover, they must heavily ...

David Atienza Alonso

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Performance evaluation of acceleration of convolutional layers on OpenEdgeCGRA

NoC Emulation on FPGA: HW/SW Synergy for NoC Features Exploration

A wearable to assess knee joint integrity using non-contact acoustic sensors

Thermal-Aware Design of of 3D ICs with Inter-Tier Liquid Cooling

Memory chip or memory array for wide-voltage range in-memory computing using bitline technology

Modelling of vertical and ferroelectric junctionless technology for efficient 3D neural network compute cube dedicated to embedded artificial intelligence

Efficient Hardware Design Of Iterative Stencil Loops

A Methodology for Embedded Classification of Heartbeats Using Random Projections

A Multi-Path Routing Strategy with Guaranteed In-order Packet Delivery and Fault Tolerance for Networks on Chips

Is the powersave governor really saving power?

Multi-Agent Reinforcement Learning for Hyperparameter Optimization of Deep Convolutional Neural Networks

Thermal-Aware Data Flow Analysis

Near-optimal thermal monitoring framework for many-core systems on chip

Heterogeneous Error-Resilient Scheme for Spectral Analysis in Ultra-Low Power Wearable Electrocardiogram Devices

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

HOLLOWS: A Power-Aware Task Scheduler for Energy Harvesting Sensor Nodes

A Machine Learning-Based Framework for Throughput Estimation of Time-Varying Applications in Multi-Core Servers

The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms

HEEPstor: an Open-Hardware Co-design Framework for Quantized Machine Learning at the Edge

Optimization of Dynamic Memory Managers for Embedded Systems Using Grammatical Evolution