Home

Person

Giovanni Ansaloni

This person has left EPFL

About
Privacy
Disclaimer

Graph Chatbot

Related publications (67)

Page 2 of 4

Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

David Atienza Alonso, Giovanni Ansaloni, Srinivasan Murali, Francisco Javier Rincon Vallejos, Ruben Braojos Lopez, Hossein Mamaghanian, Alair Dias Junior

This paper presents the system-level architecture of novel ultra-low power wireless body sensor nodes (WBSNs) for real-time cardiac monitoring and analysis, and discusses the main design challenges of this new generation of medical devices. In particular, ...

IEEE2014

Keep All in Memory with Maxwell: a Near-SRAM Computing Architecture for Edge AI Applications

David Atienza Alonso, Giovanni Ansaloni, Grégoire Axel Eggermann

Recent advances in machine learning have dramatically increased model size and computational requirements, increasingly straining computing system capabilities. This tension is particularly acute for resource-constrained edge scenarios, for which careful h ...

IEEE2025

An MLIR-based Compilation Framework for CGRA Application Deployment

David Atienza Alonso, Giovanni Ansaloni, Lara Orlandic, Juan Pablo Sapriza Araujo, Yuxuan Wang, Rubén Rodríguez Álvarez

Coarse-Grained Reconfigurable Arrays (CGRAs) are a programmable architectural solution that efficiently supports the execution of computation-intensive application functions (kernels). Compilers for CGRAs typically focus on optimizing the mapping of single ...

2025

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

2024

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Amirhossein Shahbazinia, David Atienza Alonso, Giovanni Ansaloni, Flavio Ponzina, José Angel Miranda Calero, Jonathan Dan

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

IEEE2024

Event-based sampled ECG morphology reconstruction through self-similarity

David Atienza Alonso, Giovanni Ansaloni, Tomas Teijeiro Campo, Silvio Zanoli

Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with ...

2023

Which Coupled is Best Coupled? An Exploration of AIMC Tile Interfaces and Load Balancing for CNNs

David Atienza Alonso, Giovanni Ansaloni, Irem Boybat Kara, Joshua Alexander Harrison Klein

Due to stringent energy and performance constraints, edge AI computing often employs heterogeneous systems that utilize both general-purpose CPUs and accelerators. Analog in-memory computing (AIMC) is a well-known AI inference solution that overcomes compu ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Overflow-free Compute Memories for Edge AI Acceleration

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

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 ...

Assoc Computing Machinery2023

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

David Atienza Alonso, Pierre Vandergheynst, Giovanni Ansaloni, Hossein Mamaghanian

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 ...

IEEE Press2014

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

David Atienza Alonso, Giovanni Ansaloni, Ruben Braojos Lopez, Ivan Beretta

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 ...

MDPI2014

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

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Flavio Ponzina

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, ...

2022

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

David Atienza Alonso, Giovanni Ansaloni, Pablo Garcia del Valle, Soumya Subhra Basu

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 ...

Inst Engineering Technology-Iet2016

A Reconfigurable High Dynamic Range Delta-Sigma Front End with Event-Based Decimation for Bandwidth-EfficientImplantable Neural Interfaces

David Atienza Alonso, Giovanni Ansaloni, Pasquale Davide Schiavone, Juan Pablo Sapriza Araujo

As the demand for high channel counts and high-resolution recordings of neural activity continues to grow, the increased power and data rate generated impose hard constraints on the telemetry capabilities of wireless implantable neural interfaces. To addre ...

2025

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Pengbo Yu

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

IEEE2024

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

David Atienza Alonso, Giovanni Ansaloni, Marco Antonio Rios, Grégoire Axel Eggermann

State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, ...

New York2023

Embedded Real-Time ECG Delineation Methods: a Comparative Evaluation

David Atienza Alonso, Giovanni Ansaloni, Francisco Javier Rincon Vallejos, Ruben Braojos Lopez

Wireless sensor nodes (WSNs) have recently evolved to include a fair amount of computational power, so that advanced signal processing algorithms can now be embedded even in this extremely low-power platforms. An increasingly successful field of applicatio ...

IEEE Press2012

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

David Atienza Alonso, Giovanni Ansaloni, Ruben Braojos Lopez, Soumya Subhra Basu, Loris Gérard Duch

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 ...

Assoc Computing Machinery2017

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

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Joshua Alexander Harrison Klein, Rafael Medina Morillas

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 ...

2023

Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

Alair Dias Junior, David Atienza Alonso, Giovanni Ansaloni, Srinivasan Murali, Francisco Javier Rincon Vallejos, Ruben Braojos Lopez, Hossein Mamaghanian

IEEE/ACM Press2014

ACE: Automated optimization towards iterative Classification in Edge health monitors

David Atienza Alonso, Giovanni Ansaloni, Lara Orlandic, Yuxuan Wang, Simone Machetti

Wearable devices for health monitoring are essential for tracking individuals' health status and facilitating early detection of diseases. However, processing biomedical signals online for real-time monitoring is challenging due to limited computational re ...

Institute of Electrical and Electronics Engineers (IEEE)2024