About

Pierre-Emmanuel Gaillardon is an Associate Chair and Professor in the Department of Electrical & Computer Engineering at the University of Utah. His research focuses on the development of reconfigurable logic architectures and digital circuits that exploit emerging device technologies and novel electronic design automation (EDA) techniques. His work involves advancing the design and optimization of digital systems through innovative approaches in circuit design and device integration, contributing to the evolution of reconfigurable computing and digital circuit technology.

Research topics

• Computer Science
• Artificial Intelligence
• Geography
• Statistics
• Meteorology
• Ecology
• Computer engineering
• Mathematics
• Parallel computing
• Computer hardware
• Embedded system
• Software engineering
• Computer architecture
• Environmental science

Selected publications

• Automated Generation of Microfluidic Netlists using Large Language Models

  ArXiv.org · 2026-02-22

  articleOpen accessSenior author

  Microfluidic devices have emerged as powerful tools in various laboratory applications, but the complexity of their design limits accessibility for many practitioners. While progress has been made in microfluidic design automation (MFDA), a practical and intuitive solution is still needed to connect microfluidic practitioners with MFDA techniques. This work introduces the first practical application of large language models (LLMs) in this context, providing a preliminary demonstration. Building on prior research in hardware description language (HDL) code generation with LLMs, we propose an initial methodology to convert natural language microfluidic device specifications into system-level structural Verilog netlists. We demonstrate the feasibility of our approach by generating structural netlists for practical benchmarks representative of typical microfluidic designs with correct functional flow and an average syntactical accuracy of 88%.

  PublisherOA PDF

• Automated Generation of Microfluidic Netlists using Large Language Models

  Open MIND · 2026-02-22

  preprintSenior author

  Microfluidic devices have emerged as powerful tools in various laboratory applications, but the complexity of their design limits accessibility for many practitioners. While progress has been made in microfluidic design automation (MFDA), a practical and intuitive solution is still needed to connect microfluidic practitioners with MFDA techniques. This work introduces the first practical application of large language models (LLMs) in this context, providing a preliminary demonstration. Building on prior research in hardware description language (HDL) code generation with LLMs, we propose an initial methodology to convert natural language microfluidic device specifications into system-level structural Verilog netlists. We demonstrate the feasibility of our approach by generating structural netlists for practical benchmarks representative of typical microfluidic designs with correct functional flow and an average syntactical accuracy of 88%.

  DOI

• Mechanical implementation of cryptography over Galois fields

  Cryptologia · 2026-01-20

  articleSenior author
  PublisherDOI

• Changing Idling Behavior Through Dynamic Idle Detection and Air Quality Messaging

  IEEE Internet of Things Journal · 2025-09-22 · 1 citations

  article

  Air quality impacts on human health are an increasing concern globally. Vehicle pollution is a particular concern because of its multiple adverse health effects, and discretionary vehicle idling contributes significantly to local-scale poor air quality. This study introduces a novel approach to traditional static (non-changing) anti-idling signage. Here, we demonstrate a system, called SmartAir, that provides dynamic social-norm messages to drivers coupled with information about idling status or vehicle emissions in the area. A machine learning algorithm with audio and video inputs determines vehicle idling status. Vehicle emissions are measured using a suite of low-cost air quality nodes. In this study, we show that the SmartAir system reduces idling time by 28.0% and local CO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> concentrations by 29.5% compared to background.

  PublisherDOI

• OpenMFDA: Microfluidic Design Automation in Three Dimensions

  2025-03-31

  articleSenior author

  Current microfluidic design automation (MFDA) solutions are limited by the planarity requirements of current manufacturing techniques. Recent advances in stereolithography 3D printing create an opportunity for new MFDA design methodologies. We propose a methodology for the placement of microfluidic components and the routing of flow and control channels in three dimensions. Additionally, we propose a methodology for generating a printable 3D structure from the layout. We then present OpenMFDA, an open-source MFDA design flow implementing the proposed methodologies. This design flow takes a structural netlist and produces a sliced design for manufacturing using an SLA 3D printer. Our methodology demonstrates short run times and generates devices with 2–20 x smaller area compared to state-of-the-art MFDA tools.

  PublisherDOI

• Nexus: A Lightweight and Scalable Multi-Agent Framework for Complex Tasks Automation

  ArXiv.org · 2025-02-26

  preprintOpen access

  Recent advancements in Large Language Models (LLMs) have substantially evolved Multi-Agent Systems (MASs) capabilities, enabling systems that not only automate tasks but also leverage near-human reasoning capabilities. To achieve this, LLM-based MASs need to be built around two critical principles: (i) a robust architecture that fully exploits LLM potential for specific tasks -- or related task sets -- and ($ii$) an effective methodology for equipping LLMs with the necessary capabilities to perform tasks and manage information efficiently. It goes without saying that a priori architectural designs can limit the scalability and domain adaptability of a given MAS. To address these challenges, in this paper we introduce Nexus: a lightweight Python framework designed to easily build and manage LLM-based MASs. Nexus introduces the following innovations: (i) a flexible multi-supervisor hierarchy, (ii) a simplified workflow design, and (iii) easy installation and open-source flexibility: Nexus can be installed via pip and is distributed under a permissive open-source license, allowing users to freely modify and extend its capabilities. Experimental results demonstrate that architectures built with Nexus exhibit state-of-the-art performance across diverse domains. In coding tasks, Nexus-driven MASs achieve a 99% pass rate on HumanEval and a flawless 100% on VerilogEval-Human, outperforming cutting-edge reasoning language models such as o3-mini and DeepSeek-R1. Moreover, these architectures display robust proficiency in complex reasoning and mathematical problem solving, achieving correct solutions for all randomly selected problems from the MATH dataset. In the realm of multi-objective optimization, Nexus-based architectures successfully address challenging timing closure tasks on designs from the VTR benchmark suite, while guaranteeing, on average, a power saving of nearly 30%.

  PublisherOA PDFDOI

• Adaptive Multi-Agent Reasoning via Automated Workflow Generation

  ArXiv.org · 2025-07-18

  preprintOpen access

  The rise of Large Reasoning Models (LRMs) promises a significant leap forward in language model capabilities, aiming to tackle increasingly sophisticated tasks with unprecedented efficiency and accuracy. However, despite their impressive performance, recent studies have highlighted how current reasoning models frequently fail to generalize to novel, unseen problems, often resorting to memorized solutions rather than genuine inferential reasoning. Such behavior underscores a critical limitation in modern LRMs, i.e., their tendency toward overfitting, which in turn results in poor generalization in problem-solving capabilities. In this paper, we introduce Nexus Architect, an enhanced iteration of our multi-agent system framework, Nexus, equipped with a novel automated workflow synthesis mechanism. Given a user's prompt and a small set of representative examples, the Architect autonomously generates a tailored reasoning workflow by selecting suitable strategies, tool integrations, and adversarial techniques for a specific problem class. Furthermore, the Architect includes an iterative prompt refinement mechanism that fine-tunes agents' system prompts to maximize performance and improve the generalization capabilities of the system. We empirically evaluate Nexus Architect by employing an off-the-shelf, non-reasoning model on a custom dataset of challenging logical questions and compare its performance against state-of-the-art LRMs. Results show that Nexus Architect consistently outperforms existing solutions, achieving up to a 66% increase in pass rate over Gemini 2.5 Flash Preview, nearly 2.5$\times$ against Claude Sonnet 4 and DeepSeek-R1, and over 3$\times$ w.r.t. Llama 4 Scout.

  PublisherOA PDF

• ARIANNA: An Automatic Design Flow for Fabric Customization and eFPGA Redaction

  ACM Transactions on Design Automation of Electronic Systems · 2025-06-02 · 1 citations

  articleOpen access

  In the modern global Integrated Circuit (IC) supply chain, protecting intellectual property (IP) is a complex challenge, and balancing IP loss risk and added cost for theft countermeasures is hard to achieve. Using embedded configurable logic allows designers to completely hide the functionality of selected design portions from parties that do not have access to the configuration string (bitstream). However, the design space of redacted solutions is huge, with tradeoffs between the portions selected for redaction and the configuration of the configurable embedded logic. We propose ARIANNA, a complete flow that aids the designer in all the stages, from selecting the logic to be hidden to tailoring the bespoke fabrics for the configurable logic used to hide it. We present a security evaluation of the considered fabrics and introduce two heuristics for the novel bespoke fabric flow. We evaluate the heuristics against an exhaustive approach. We also evaluate the complete flow using a selection of benchmarks. Results show that using ARIANNA to customize the redaction fabrics yields up to 3.3× lower overheads and 4× higher eFPGA fabric utilization than a one-fits-all fabric as proposed in prior works.

  PublisherOA PDFDOI

• An open source platform to automate the design, verification, and manufacture of 3D printed microfluidic devices

  Scientific Reports · 2025-09-26 · 2 citations

  articleOpen access

  Microfluidic devices, including lab-on-a-chip devices, have many advantages over conventional laboratory techniques. Although common in some applications, there are several barriers to wider adoption, including the high initial cost to fabricate a design and the labor-intensive process of development. This work seeks to address those barriers by combining 3D printing and computer aided design tools. Building on existing open-source software from electronic design automation (EDA), we present a design, verification, and manufacturing toolchain for 3D design for microfluidic devices. The process starts with a list of components and connections then, automatically lays out a microfluidic device using a library of components, simulates the device, and produces a 3D CAD file that is used for DLP 3D printing process. The automated design and fabrication process was demonstrated by automatically designing and fabricating a calcium quantification assay. This toolchain automatically generated a microfluidic chip that meters each reagent with an error of less than 2.24% as verified by Xyce simulation of the chip. Physical chips were printed and found to perform with errors less than 9.2% on average compared to the assay performed by hand. The demonstration showed the ability of the toolchain to automatically generate a functional microfluidic chip for use with real assays using previously developed EDA tools.

  PublisherOA PDFDOI

• A Low SWaP, Low-Cost, Multi-Chemical Monitoring System

  2025-04-23

  articleSenior author

  Prior research has shown that ambient air contains more than 3,000 different pollutants. However, current environmental sensor networks (ESNs) predominantly focus on monitoring only six pollutants, as guided by the EPA and WHO. This leaves numerous other chemicals unmonitored, resulting in limited knowledge about their background levels and safe thresholds. To address this, we propose an innovative system focusing on collecting data on a wide range of chemicals that are not commonly measured. The proposed system can be deployed independently or be integrated into currently deployed ESN nodes to broaden the range of chemicals they monitor. To enable this, it features a compact form factor with low weight, power consumption, and a small manufacturing cost. The system implements up to 12 chemiresistive nanofiber sensors designed to target different chemicals. These sensors are interfaced by a specially designed ASIC optimized to accommodate the sensors’ wide range of resistance values, up to ten gigaohms, while maintaining low power consumption and high measurement accuracy. The 15 g chemical monitoring front-end operates on a mere 52 mW total using only 4.3 mW per sensor and occupies a volume of 30 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>, with an affordable cost of $95 per each system. An exploratory trial of the system demonstrate 86.69% accuracy on average in differentiating exposed chemicals.

  PublisherDOI

Recent grants

• CAREER: Functionality-Enhanced Devices for Extending Moore's Law

  NSF · $508k · 2018–2023

• EAGER: Ultra-High-Performance Terahertz Detection Exploiting Super-Steep-Subthreshold-Slope (S4)-FinFETs

  NSF · $150k · 2016–2018

Frequent coauthors

• Fabien Clermidy

  Commissariat à l'Énergie Atomique et aux Énergies Alternatives

  222 shared

• Giovanni De Micheli

  École Polytechnique Fédérale de Lausanne

  157 shared

• M. Haykel Ben-Jamaa

  100 shared

• Ian O’Connor

  École d'Ingénieurs en Chimie et Sciences du Numérique

  97 shared

• Luca Amarú

  Synopsys (United States)

  70 shared

• L. Perniola

  CEA LETI

  51 shared

• Marina Reyboz

  Commissariat à l'Énergie Atomique et aux Énergies Alternatives

  50 shared

• Giovanni Betti Beneventi

  50 shared

Labs

• Utah Nanofab Utah Robotics Center U-Smart Energy LaboratoryPI