Who? Martin Andraud

What? Assistant professor

Where? UCLouvain (BE) & Aalto University (FI)

Contact? martin.andraud [at] uclouvain [.] be

Research interests

Processors (accelerators) for edge AI 85%
Probabilistic AI models 95%
Adaptation algorithms for IoT systems 50%
About me

I am an assistant professor at UCLouvain, Belgium, since January 2024, and a visting professor at Aalto University, Finland. My research interests are generally centered around hardware for Artificial Intelligence, at the interface between algorithms and circuit design.

I obtained my doctoral degree in TIMA lab, Grenoble-Alpes University, France, in 2016, supervised by Emmanuel Simeu and Haralampos Stratigopoulos. I then worked as a post-doc successively in TU Eindhoven and KU Leuven from 2016 to 2019. Prior to joining UCLouvain in 2024, I was an assistant professor at Aalto University between 2019 and 2023.

I am always interested in collaborating, please do not hesitate to get in touch.

Fresh In

Paper accepted at UAI 2024

Our paper "On Hardware-efficient Inference in Probabilistic Circuits" ( with Lingyun Yao, Martin Trapp, Jelin Leslin, Gaurav Singh, Peng Zhang, and Karthekeyan Periasamy) has been accepted at the UAI conference, which will be held in July 2024. Blog info will follow.

Paper accepted at IEEE NEWCAS 2024

Our paper "AutoPC: an open-source framework for efficient probabilistic reasoning on FPGA hardware" (with Karthekeyan Periasamy, Jelin Leslin, Aleksi Korsman, and Lingyun Yao) has been accepted at the IEEE NEWCAS, which will be held in June 2024. Blog info will follow.

I started at UCLouvain!

Pretty big news, I officially started in January 2024 as an assistant professor at UCLouvain, in Belgium. I will continue my work at Aalto as a visiting professor, stay tuned!

NorCAS 2023

Our paper "Toward All-Digital Time-Domain Neural Network Accelerators for In-Sensor Processing Applications" (with Ahmed Mohey, Marko Kosunen and Jussi Ryynänen) has been presented at NorCAS 2023 Paper here

Open Positions

Open Positions

Doctoral candidate in mixed-signal integrated circuit design for test and reliability of in-memory computing AI accelerators with emerging devices

Open PhD Position

Martin A.
15.07.2024
Open Positions

Doctoral candidate in IC design for heterogenous AI accelerators

Open PhD Position

Martin A.
15.07.2024

Latest publications

You can find me on Google scholar

Peer-reviewed journals

(J7) J. De Roose, M.Andraud, M.Verhelst, “A procedural method to predictively assess power-quality trade-offs of circuit-level adaptivity in IoT systems”, Frontiers in Electronics, May 2022.

(J6) Mahmood, N.H., Böcker, S., Moerman, I., López, O.A., Munari, A., Mikhaylov, K., Clazzer, F., Bartz, H., Park O., Mercier, E., Saidi, S., Diana Moya Osorio1, Jäntti, R., Pragada, R., Annanperä, E., Ma, Y., Wietfeld C., Andraud, M., Liva, G., Chen, Y., Garro, E., Burkhardt, F., Liu, C-F, Alves, H., Said, Y., Kelanti, M., Doré, J-B., Kim, E., Shin J., Park J-G, Kim, S-K., Yoon, C., Anwar, K. and Seppänen, P., “Machine type communications: key drivers and enablers towards the 6G era”. J Wireless Com Network, 2021.

Peer-reviewed conferences

(C18) A. M. Mohey, M. Kosunen, J. Ryynänen and M. Andraud, "Toward All-Digital Time-Domain Neural Network Accelerators for In-Sensor Processing Applications," 2023 IEEE Nordic Circuits and Systems Conference (NorCAS) 2023

(C17) O. Numan, M. Andraud, K. Halonen, "A Self-Calibrated Activation Neuron Topology for Efficient Resistive-Based In-Memory Computing" VLSI-SoC 2023

(C16) D. Monga, O. Numan, M. Andraud, K. Halonen, "A temperature and process compensation circuit for resistive-based in-memory computing arrays" ISCAS 2023

(C15) J. Leslin, A. Hyttinen, K. Periasamy, L. Yao, M. Trapp, M. Andraud, “A Hardware Perspective to Evaluating Probabilistic Circuits” A hardware perspective to evaluating probabilistic circuits,” in Proceedings of the 11th International Conference on Probabilistic Graphical Models, ser. Proceedings of Machine Learning Research, vol. 186. PMLR, 2022, pp. 349–360

(C14) N. Xama, J. Raymaekers, M. Andraud, J. Gomez, W. Dobbelaere, R. Vanhooren, A. Coyette, G. Gielen, “Avoiding Mixed-Signal Field Returns by Outlier Detection of Hard-to-Detect Defects based on Multivariate Statistics”, IEEE European Test Symposium (ETS), May 2020.

Other contributions

(O3) L. Yao, M. Trapp, K. Periasamy, J. Leslin, G. Singh, M. Andraud, “Logarithm-Approximate Floating-Point Multiplier for Hardware-efficient Inference in Probabilistic Circuits”, 6th workshop on Tractable Probabilistic Modelling, collocated with the Uncertainty in Artificial Intelligence (UAI) conference, August 2023

Sponsored research

(SustAIn) Smart Building Sensitive to Daily Sentiment

EU EiC Pathfinder project | 2022-2026

Keywords : Autonomous sensor nodes, distributed intelligence, AI accelerators, probabilistic models, probabilistic circuits, self-adaptive circuits

(A-core) Aalto RISC-V processor core with embedded AI accelerators

Internal collaboration / 2021-...

Keywords : AI accelerators, In-memory Computing, RISC-V processor

(WHISTLE) When integrated systems gain life experience: towards self-learning circuits with resource-efficient embedded artificial intelligence

Academy of Finland | 2020-2024

Keywords : Probabilistic models, probabilistic circuits, self-adaptive circuits, self-learning circuits

(DIFFER) Digital-intensive IC sensor interface for frequency-modulated gyroscopes

Industry collaboration / 2021-2023

Keywords : Sensor interface, IC design, time-based circuits

(PiCo) Processing in contact, High Density, Ultra Low Power Computing Element

Industry collaboration / 2020-2022

Keywords : AI accelerator, convolutional neural networks, analog computing

(ferrARI) Ferroelectric-enhanced Analog accelerators for Robust, ultra-low power edge Intelligence.

Finnish Research Council / 2024-2027

Keywords : AI accelerator, analog computing, Emerging memories

Blog

Papers we write

Monga et al., ISCAS'23

A temperature and process compensation circuit for resistive-based in-memory computing arrays" By Dipesh Monga et al., ISCAS'23

Martin A.
10.07.2023
Papers I Read

TeAAL

"TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators", by Nandeeka Nayak et al.

Martin A.
10.07.2023

Team

Current doctoral students

Kazybek Adam (Supervisor), “In Memory Computing architecture for fully-analog on-chip machine learning accelerators”. Starting date: Oct. 2020

Karthekeyan Periasamy (Supervisor), “Custom hardware accelerators for on-chip probabilistic machine learning”. Starting date: April 2021

Jelin Leslin (Supervisor), “Probabilistic Machine Learning Hardware Architectures towards Self Learning Edge AI”. Starting date: May 2021

Ahmed Mohey (Supervisor), “Integrated Circuit (IC) Architectures for Novel Time-Based Sensor Interfaces”. Starting date: September 2021

Lingyun Yao (Supervisor). “Hardware-accelerated Probabilistic circuits for probabilistic edge AI”. Starting date: October 2022

Omar Numan (Advisor, supervisor Prof. Kari Halonen), “Design of analog and analog-mixed-signal integrated-circuits for analog in-memory computing for AI applications”. Starting date: November 2020

Gaurav Singh (Advisor, supervisor Prof. Kari Halonen) “Signal processing enabled system-on-chip for wireless-transfer of critical sensor data at low power” - Starting date: January 2019

Current master thesis students