Publications related to Accurate deep neural network inference using computational phase-change memory

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

EPFL2024

EdgeAI-Aware Design of In-Memory Computing Architectures

Marco Antonio Rios

Driven by the demand for real-time processing and the need to minimize latency in AI algorithms, edge computing has experienced remarkable progress. Decision-making AI applications stand out for their heavy reliance on data-centric operations, predominantl ...

EPFL2024

Towards Scalable Electronics: Synthetic 2D Materials for Large-Area 2D Circuit Integration

Zhenyu Wang

In the past decades, a significant increase of the transistor density on a chip has led to exponential growth in computational power driven by Moore's law. To overcome the bottleneck of traditional von-Neumann architecture in computational efficiency, effo ...

EPFL2024

Magnon-Assisted Magnetization Reversal of Ni81Fe19 Nanostripes on Y3Fe5O12 with Different Interfaces

Dirk Grundler, Andrea Mucchietto, Korbinian Baumgärtl

Magnetic bit writing by short-wave magnons without conversion to the electrical domain is expected to be a game-changer for in-memory computing architectures. Recently, the reversal of nanomagnets by propagating magnons was demonstrated. However, experimen ...

2024

Secure Interface Design Leveraging Hardware/Software Support

Atri Bhattacharyya

Computer systems rely heavily on abstraction to manage the exponential growth of complexity across hardware and software. Due to practical considerations of compatibility between components of these complex systems across generations, developers have favou ...

EPFL2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Predicting the long-term collective behaviour of fish pairs with deep learning

Francesco Mondada, Alexandre Massoud Alahi, Vaios Papaspyros

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

2024

Robust NAS under adversarial training: benchmark, theory, and beyond

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Yongtao Wu

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

2024

Understanding generalization and robustness in modern deep learning

Maksym Andriushchenko

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Marco Picasso, Alexandre Caboussat, Maude Girardin

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Lausanne2024

Accurate deep neural network inference using computational phase-change memory

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Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

EdgeAI-Aware Design of In-Memory Computing Architectures

Towards Scalable Electronics: Synthetic 2D Materials for Large-Area 2D Circuit Integration

Magnon-Assisted Magnetization Reversal of Ni81Fe19 Nanostripes on Y3Fe5O12 with Different Interfaces

Secure Interface Design Leveraging Hardware/Software Support

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Predicting the long-term collective behaviour of fish pairs with deep learning

Robust NAS under adversarial training: benchmark, theory, and beyond

Understanding generalization and robustness in modern deep learning

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

EdgeAI-Aware Design of In-Memory Computing Architectures

Towards Scalable Electronics: Synthetic 2D Materials for Large-Area 2D Circuit Integration

Magnon-Assisted Magnetization Reversal of Ni81Fe19 Nanostripes on Y3Fe5O12 with Different Interfaces

Secure Interface Design Leveraging Hardware/Software Support

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Robust NAS under adversarial training: benchmark, theory, and beyond

Predicting the long-term collective behaviour of fish pairs with deep learning

Understanding generalization and robustness in modern deep learning

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE