About

Francesco Restuccia is an Assistant Professor of Electrical and Computer Engineering at Northeastern University College of Engineering. His research focuses on creating new and unconventional pathways to enhance the performance and resilience of intelligent cyber-physical systems beyond current capabilities. His work is supported by research grants from the National Science Foundation and the Department of Defense. Restuccia has received numerous awards, including the 2025 DARPA Young Faculty Award, 2023 ONR Young Investigator Award, 2023 AFOSR Young Investigator Award, and the 2019 Mario Gerla Award in Computer Science. He is a Senior Member of IEEE and ACM and actively serves on technical program committees and editorial boards of prestigious journals and conferences.

Research topics

• Computer Science
• Computer network
• Telecommunications
• Computer Security
• Artificial Intelligence
• Embedded system
• Computer hardware
• Computer architecture
• Operating system

Selected publications

• Critical Analysis of Energy Consumption in Neuro-Computational Systems

  IEEE Access · 2026-01-01

  articleOpen access

  This article presents a unified benchmark of energy consumption per synaptic event across five classes of neuro-computational substrates: graphics processing units, neural processing units, field-programmable gate arrays, digital spiking processors, and in-memory/memristive devices, with biological synapses used as reference bands. The main contribution is methodological as well as empirical. Methodologically, we introduce a common event-level metric that makes heterogeneous systems directly comparable despite major differences in architecture, coding scheme, and learning dynamics. Empirically, we combine measurements obtained by the authors with carefully normalized literature data to map the present energy landscape of artificial and biological neural computation. The results reveal three robust regimes. Dense von Neumann ANN implementations on GPUs, NPUs, and FPGA operate mainly in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{5}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{8}$ </tex-math></inline-formula> fJ range per synaptic event. Digital spiking processors reduce this requirement to about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{3}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{4}$ </tex-math></inline-formula> fJ per event. Memristive and transistor-based artificial synapses span a much broader interval, from array-level values near <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{6}$ </tex-math></inline-formula> fJ down to a few femtojoules in the most efficient device-level realizations. In particular, the best organic PANI memristive samples approach ~2 fJ per synaptic event, entering the biological vicinity and, in some cases, surpassing the rat reference range. Taken together, the benchmarked landscape spans up to ten orders of magnitude across the full set of systems considered in this study. This result clarifies where current AI hardware stands relative to biological efficiency, identifies event-driven and in-memory computation as the most promising routes toward sustainable AI, and provides a quantitatively grounded reference framework for future neuromorphic benchmarking. INDEX TERMS Artificial neural networks, energy consumption, energy efficiency, memristive devices, neuromorphic computing, spiking neural networks, sustainable AI.

  PublisherDOI

• Semantic Edge Computing and Semantic Communications in 6g Networks: A Unifying Survey and Research Challenges

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• A Low-Power Differential Temperature Sensor with Chopped Cascode Transistors and Switched-Capacitor Integration

  Electronics · 2025-06-11

  articleOpen access

  Embedded differential temperature sensors can be utilized to monitor the power consumption of circuits, taking advantage of the inherent on-chip electrothermal coupling. Potential applications range from hardware security to linearity, gain/bandwidth calibration, defect-oriented testing, and compensation for circuit aging effects. This paper introduces the use of on-chip differential temperature sensors as part of a wireless Internet of Things system. A new low-power differential temperature sensor circuit with chopped cascode transistors and switched-capacitor integration is described. This design approach leverages chopper stabilization in combination with a switched-capacitor integrator that acts as a low-pass filter such that the circuit provides offset and low-frequency noise mitigation. Simulation results of the proposed differential temperature sensor in a 65 nm complementary metal-oxide-semiconductor (CMOS) process show a sensitivity of 33.18V/°C within a linear range of ±36.5m°C and an integrated output noise of 0.862mVrms (from 1 to 441.7 Hz) with an overall power consumption of 0.187mW. Considering a figure of merit that involves sensitivity, linear range, noise, and power, the new temperature sensor topology demonstrates a significant improvement compared to state-of-the-art differential temperature sensors for on-chip monitoring of power dissipation.

  PublisherOA PDFDOI

• Si-FI: Learning the Beamforming Feedback for Simultaneous Multi-Subject Sensing

  Computer Networks · 2025-12-03

  articleOpen accessSenior authorCorresponding

  There has been significant progress in Wi-Fi sensing applications pertaining to home surveillance, remote healthcare, and home entertainment among others. However, most of the work leverages manual extraction of Channel State Information (CSI) from Wi-Fi network interface card (NIC) and targets single-subject sensing. In this work, we devise a simultaneous multi-subject sensing strategy that can adapt to different environments and people being monitored. Si-FI leverages standard-compliant beamforming feedback information (BFI) as a proxy of CSI to characterize the propagation environment. Unlike CSI, BFI (i) can be captured without any firmware modifications and (ii) captures the multiple channels between the access point and the stations without any direct access to the sensing devices. Thus, conversely, from existing work, the edge server in Si-FI records the BFI of the channels between Access Point (AP) and all the stations (STAs) (sensing devices) with a single capture, reducing the channel occupation, transmission and system latency dramatically. To achieve generalization over unseen environments and people, we develop a few-shot learning algorithm named Si-FI FREL to operate with beamforming feedback angles (BFAs) (compressed BFI). We validate Si-FI through an extensive data collection campaign in 3 different environments and 3 subjects performing 20 different activities simultaneously. We demonstrate that Si-FI achieves classification accuracy of up to 99 %, while Si-FI FREL improves the accuracy up to 27 % when compared to the state-of-the-art domain adaptation algorithm. Si-FI reduces the system latency by 50 % and channel occupation by 110 KB per sample for each sensing device compared to the state-of-the-art simultaneous multi-subject sensing work.

  PublisherDOI

• SHRINK: Reducing MIMO Feedback Overhead in Wi-Fi with Dynamic Data-Driven Channel Sounding

  2025-10-23

  articleOpen accessSenior author

  The performance of multiple-input, multiple-output (MIMO) systems highly depends on the precision of channel estimates provided by the mobile users. However, the current Wi-Fi standard requires an update interval of 10 ms, irrespective of the channel dynamics. This imposes a substantial overhead for the MIMO channel estimation. Recent work mainly targets different compression strategies, potentially compromising precoding accuracy and, in turn, the network performance. In stark opposition, we propose SHRINK, a framework to dynamically adapt the feedback transmission rate to the propagation environments and performance requirements. SHRINK determines whether the users should send back their channel estimates by predicting network performance through a data-driven analysis of prior and current channel estimates. We have experimentally evaluated SHRINK using off-the-shelf Wi-Fi devices in multiple environments, including an anechoic chamber, and benchmarked its performance against several state-of-the-art approaches. Experimental results show that SHRINK reduces airtime and data overhead by 81% on average compared to the IEEE 802.11 standard without impacting the precoding performance. Moreover, SHRINK outperforms state-of-the-art approaches by an average gain of 33.6% in airtime and data overhead reduction, corresponding to an increase in throughput of 24.5%.

  PublisherOA PDFDOI

• 6G Horizons: Toward Intelligent and Sustainable Connectivity [From the Guest Editors]

  IEEE Vehicular Technology Magazine · 2025-03-01

  article
  PublisherDOI

• SCOPE: Cooperative Integrated Communications and Sensing for Material Classification at Sub-Terahertz Frequencies

  IEEE Wireless Communications Letters · 2025-06-02

  articleSenior author

  In this letter, we propose SCOPE—a novel, entropyweighted ensembling approach for material classification at sub-Terahertz (THz) frequencies. Unlike existing methods that primarily use dedicated radars, SCOPE builds upon an integrated communication and sensing system and leverages information from both penetrating and reflected signals to enhance spatial resolution and detection accuracy across environments. We adopted spatial variability augmentation (SVA) to address the challenge of generalization across varying transmission distances and antenna gains. While most prior works are limited to radar systems or simulations, SCOPE is implemented and validated in a real sub-THz system working with a 10 GHz bandwidth. Our assessments across different sensing distances, antenna gains, and channel conditions demonstrate the efficacy of SCOPE, which reaches up to 99% accuracy in detecting five materials—glass, wood, metal, air, and plastic—outperforming existing techniques. To facilitate reproducibility, our dataset and code are available at: https://github.com/kfoysalhaque/SCOPE.

  PublisherDOI

• Semantic Edge Computing and Semantic Communications in 6G networks: A unifying survey and research challenges

  Computer Networks · 2025-07-18 · 6 citations

  articleSenior author
  PublisherDOI

• Digitally Tunable CMOS Mixer Design for Adaptive RF Front-Ends

  2025-05-25 · 2 citations

  article

  Reconfigurability and self-optimization have become essential during radio frequency integrated circuit (RFIC) design to support the growing number of devices and fast changes in the surrounding wireless spectrum. This has created the need to develop new design approaches for RFICs based on end-to-end wireless system-level performance metrics during operation in dynamically changing communication environments. This paper introduces a CMOS mixer with a wide range of digitally tunable bias current for machine learning (ML) based adaptation. The mixer is designed to become part of a self-adaptive receiver (RX) architecture that is capable of optimizing its performance in accordance to wireless channel conditions by evaluating systemlevel parameters. The proposed mixer topology has digitally tunable bias current and a programmable helper current (PHC) circuit to maintain the voltage headroom during operation with a wide tuning range. It is capable of dynamically minimizing power consumption based on performance and wireless network requirements. Post-layout simulation results show that the power consumption can be reduced up to 8x depending on momentary performance needs, while maintaining an IIP3 greater than -2.4 dBm for the entire range of operation.

  PublisherDOI

• How to Poison an xApp: Dissecting Backdoor Attacks to Deep Reinforcement Learning in Open Radio Access Networks

  Computer Networks · 2025-09-24

  articleOpen accessSenior author

  The development of Open Radio Access Network (RAN) cellular systems is being propelled by the integration of Artificial Intelligence (AI) techniques. While AI can enhance network performance, it expands the attack surface of the RAN. For instance, the need for datasets to train AI algorithms and the use of open interface to retrieve data in real time paves the way to data tampering during both training and inference phases. In this work, we propose MalO-RAN, a framework to evaluate the impact of data poisoning on O-RAN intelligent applications. We focus on AI-based xApps taking control decisions via Deep Reinforcement Learning (DRL), and investigate backdoor attacks, where tampered data is added to training datasets to include a backdoor in the final model that can be used by the attacker to trigger potentially harmful or inefficient pre-defined control decisions. We leverage an extensive O-RAN dataset collected on the Colosseum network emulator and show how an attacker may tamper with the training of AI models embedded in xApps, with the goal of favoring specific tenants after the application deployment on the network. We experimentally evaluate the impact of the SleeperNets and TrojDRL attacks and show that backdoor attacks achieve up to a 0.9 attack success rate. Moreover, we demonstrate the impact of these attacks on a live O-RAN deployment implemented on Colosseum, where we instantiate the xApps poisoned with MalO-RAN on an O-RAN-compliant Near-real-time RAN Intelligent Controller (RIC). Results show that these attacks cause an average network performance degradation of 87%.

  PublisherDOI

Recent grants

• Collaborative Research: NeTS: Small: Reliable Task Offloading in Mobile Autonomous Systems Through Semantic MU-MIMO Control

  NSF · $310k · 2021–2025

• CCF: SHF: Small: Self-Adaptive Interference-Avoiding Wireless Receiver Hardware through Real-Time Learning-Based Automatic Optimization of Power-Efficient Integrated Circuits

  NSF · $600k · 2022–2026

• Collaborative Research: SWIFT: AI-based Sensing for Improved Resiliency via Spectral Adaptation with Lifelong Learning

  NSF · $304k · 2023–2026

• Collaborative Research: FuSe: Deep Learning and Signal Processing using Silicon Photonics and Digital CMOS Circuits for Ultra-Wideband Spectrum Perception

  NSF · $487k · 2023–2026

Frequent coauthors

• Tommaso Melodia

  83 shared

• Salvatore D’Oro

  52 shared

• Marco Cominelli

  Politecnico di Milano

  17 shared

• Francesco Gringoli

  University of Brescia

  17 shared

• Jonathan Ashdown

  United States Air Force Research Laboratory

  15 shared

• Francesca Meneghello

  University of Padua

  15 shared

• Marco Levorato

  14 shared

• Amani Al-Shawabka

  14 shared

Labs

• MENTIS LaboratoryPI

Awards & honors

• 2026 IEEE INFOCOM Best Paper Award
• 2025 DARPA Young Faculty Award
• 2025 IEEE INFOCOM Best Paper Award
• 2025 Søren Buus Outstanding Research Award
• 2023 AFOSR Young Investigator Award