Publications associées à Peephole optimization

Mesh-based topology, shape and sizing optimization of ribbed plates

In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

Springer2024

Scalable constrained optimization

Maria-Luiza Vladarean

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

EPFL2024

MOD2IR: High-Performance Code Generation for a Biophysically Detailed Neuronal Simulation DSL

Felix Schürmann, Pramod Shivaji Kumbhar, Omar Awile, Snehal Pramod Kumbhar, Ioannis Magkanaris

Advances in computational capabilities and large volumes of experimental data have established computer simulations of brain tissue models as an important pillar in modern neuroscience. Alongside, a variety of domain specific languages (DSLs) have been dev ...

ACM2023

WarpAttack: Bypassing CFI through Compiler-Introduced Double-Fetches

Mathias Josef Payer, Flavio Toffalini, Luca Di Bartolomeo, Jianhao Xu

Code-reuse attacks are dangerous threats that attracted the attention of the security community for years. These attacks aim at corrupting important control-flow transfers for taking control of a process without injecting code. Nowadays, the combinations o ...

IEEE COMPUTER SOC2023

Stable Nonconvex-Nonconcave Training via Linear Interpolation

Volkan Cevher, Thomas Michaelsen Pethick, Wanyun Xie

This paper presents a theoretical analysis of linear interpolation as a principled method for stabilizing (large-scale) neural network training. We argue that instabilities in the optimization process are often caused by the nonmonotonicity of the loss lan ...

2023

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Martin Jaggi, Thijs Vogels, Hadrien Hendrikx

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers commu ...

Microtome Publishing2023

Silent Bugs Matter: A Study of Compiler-Introduced Security Bugs

Mathias Josef Payer, Jianhao Xu

Compilers assure that any produced optimized code is semantically equivalent to the original code. However, even "correct" compilers may introduce security bugs as security properties go beyond translation correctness. Security bugs introduced by such corr ...

Usenix Assoc2023

Modeling and Design Optimization of Loosely Coupled PCB Spiral Coils in Inductive Power Transfer Systems

Drazen Dujic, Xiaotong Du

There are various possibilities to realize coil winding designs for an inductive power transfer system. In order to achieve high power transfer efficiency and power density and explore trade-offs between the two, design optimization around the coil link is ...

2023

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Dimitrios Kaminaris

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. ...

MDPI2023

Structural topology exploration through policy-based generation of equilibrium representations

Corentin Jean Dominique Fivet, Ioannis Mirtsopoulos

Mainstream approaches to design spatial architectural forms that are structurally relevant consist either in adapting well-known and catalogued conventional types or in searching for close-to-optimum solutions of well-defined problems. Few means exist to e ...

2023