17 citations · 48 across the 10 of their papers we have counts for
10 papers
BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop
Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo +2
This paper introduces BearingNAS, a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift the intelligence directly onto the sensor die via in-sensor proce…
Leveraging systems' non-linearity to tackle the scarcity of data in the design of Intelligent Fault Diagnosis Systems
Giancarlo Santamato, Andrea Mattia Garavagno, Massimiliano Solazzi +1
Deep Transfer Learning (DTL) allows for the efficient building of Intelligent Fault Diagnosis Systems (IFDS). On the other hand, DTL methods still heavily rely on large amounts of…
An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms
Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli +1
Hardware-aware neural architecture search (HW-NAS) allows the integration of Convolutional Neural Networks (CNNs) in microcontrollers devices by automatically designing neural arch…
On-Device Neural Architecture Search
Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo +2
This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best…
Running hardware-aware neural architecture search on embedded devices under 512MB of RAM
Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo +1
This document proposes a novel approach to hardware-aware neural architecture search (HW NAS) that considers the resources available on the computing platform running it, enabling…
Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis
Jacob Huckelberry, Andrea Mattia Garavagno, Yuke Zhang +3
Most TinyML hardware accelerators focus on supporting Quantized Neural Networks (QNNs) to meet stringent constraints on power consumption and size. Despite this, the security aspec…
QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture
Shvetank Prakash, Andrew Cheng, Mark Mazumder +24
The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) ev…
Searching Neural Architectures for Sensor Nodes on IoT Gateways
Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli +1
This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT…
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications
Colby Banbury, Emil Njor, Andrea Mattia Garavagno +7
Tiny machine learning (TinyML) co-locates models with sensors on microcontrollers, where small models (which are disproportionately sensitive to label noise) and bespoke binary tas…
Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor
Andrea Mattia Garavagno, Daniele Leonardis, Antonio Frisoli
The current trend of applying transfer learning from convolutional neural networks (CNNs) trained on large datasets can be an overkill when the target application is a custom and d…