136 citations · 283 across the 19 of their papers we have counts for
19 papers
Enabling Ultra-Low-Power Always-On Feedforward Leakage Suppression Logic Circuits with FDSOI
Clément Choné, Leslie Xu, Filippo Quadri +5
The growing deployment of real-time applications on wearable and Internet of Things (IoT) edge devices has intensified the need for energy-efficient, high-performance systems that…
X-HEEP: An Open-Source, Configurable and Extendible RISC-V Platform for TinyAI Applications
Simone Machetti, Pasquale Davide Schiavone, Giovanni Ansaloni +2
In this work, we present X-HEEP, an open-source, configurable, and extendible RISC-V platform for ultra-low-power edge applications (TinyAI). X-HEEP features the eXtendible Acceler…
e-GPU: An Open-Source and Configurable RISC-V Graphic Processing Unit for TinyAI Applications
Simone Machetti, Pasquale Davide Schiavone, Lara Orlandic +4
Graphics processing units (GPUs) excel at parallel processing, but remain largely unexplored in ultra-low-power edge devices (TinyAI) due to their power and area limitations, as we…
Quadrilatero: A RISC-V programmable matrix coprocessor for low-power edge applications
Danilo Cammarata, Matteo Perotti, Marco Bertuletti +4
The rapid growth of AI-based Internet-of-Things applications increased the demand for high-performance edge processing engines on a low-power budget and tight area constraints. As…
Just TestIt! An SBST Approach To Automate System-Integration Testing
Tommaso Terzano, Luigi Giuffrida, Juan Sapriza +5
This paper introduces TestIt, an open-source Python package designed to automate full-system integration testing using a Software-Based Self-Test (SBST) approach. By dynamically ge…
ARCANE: Adaptive RISC-V Cache Architecture for Near-memory Extensions
Vincenzo Petrolo, Flavia Guella, Michele Caon +3
Modern data-driven applications expose limitations of von Neumann architectures - extensive data movement, low throughput, and poor energy efficiency. Accelerators improve performa…
Increasing the Energy-Efficiency of Wearables Using Low-Precision Posit Arithmetic with PHEE
David Mallasén, Pasquale Davide Schiavone, Alberto A. Del Barrio +2
Wearable edge AI biomedical devices are increasingly being used for continuous patient health monitoring, enabling real-time insights and extended data collection without the need…
Enabling Efficient Wearables: An Analysis of Low-Power Microcontrollers for Biomedical Applications
Dimitrios Samakovlis, Stefano Albini, Rubén Rodríguez Álvarez +4
Breakthroughs in ultra-low-power chip technology are transforming biomedical wearables, making it possible to monitor patients in real time with devices operating on mere μW. Altho…
MetaWearS: A Shortcut in Wearable Systems Lifecycle with Only a Few Shots
Alireza Amirshahi, Maedeh H. Toosi, Siamak Mohammadi +5
Wearable systems provide continuous health monitoring and can lead to early detection of potential health issues. However, the lifecycle of wearable systems faces several challenge…
Scalable and RISC-V Programmable Near-Memory Computing Architectures for Edge Nodes
Michele Caon, Clément Choné, Pasquale Davide Schiavone +4
The widespread adoption of data-centric algorithms, particularly Artificial Intelligence (AI) and Machine Learning (ML), has exposed the limitations of centralized processing infra…
BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables
Dimitrios Samakovlis, Stefano Albini, Rubén Rodríguez Álvarez +4
The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-tim…
STRELA: STReaming ELAstic CGRA Accelerator for Embedded Systems
Daniel Vazquez, Jose Miranda, Alfonso Rodriguez +3
Reconfigurable computing offers a good balance between flexibility and energy efficiency. When combined with software-programmable devices such as CPUs, it is possible to obtain hi…
X-HEEP: An Open-Source, Configurable and Extendible RISC-V Microcontroller for the Exploration of Ultra-Low-Power Edge Accelerators
Simone Machetti, Pasquale Davide Schiavone, Thomas Christoph Müller +2
The field of edge computing has witnessed remarkable growth owing to the increasing demand for real-time processing of data in applications. However, challenges persist due to limi…
Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node
Alfio Di Mauro, Francesco Conti, Pasquale Davide Schiavone +2
Binary Neural Networks (BNNs) have been shown to be robust to random bit-level noise, making aggressive voltage scaling attractive as a power-saving technique for both logic and SR…
Arnold: an eFPGA-Augmented RISC-V SoC for Flexible and Low-Power IoT End-Nodes
Pasquale Davide Schiavone, Davide Rossi, Alfio Di Mauro +5
A wide range of Internet of Things (IoT) applications require powerful, energy-efficient and flexible end-nodes to acquire data from multiple sources, process and distill the sense…
XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference
Francesco Conti, Pasquale Davide Schiavone, Luca Benini
Binary Neural Networks (BNNs) are promising to deliver accuracy comparable to conventional deep neural networks at a fraction of the cost in terms of memory and energy. In this pap…
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
Michael Hersche, Tino Rellstab, Pasquale Davide Schiavone +3
Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interfa…
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Francesco Conti, Robert Schilling, Pasquale Davide Schiavone +9
Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns,…
A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devices
Michael Gautschi, Pasquale Davide Schiavone, Andreas Traber +6
Endpoint devices for Internet-of-Things not only need to work under extremely tight power envelope of a few milliwatts, but also need to be flexible in their computing capabilities…