High System-Code Security with Low Overhead

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Publications associées (32)

A Deep-Learning Approach to Side-Channel Based CPU Disassembly at Design Time

Mirjana Stojilovic

Side-channel CPU disassembly is a side-channel attack that allows an adversary to recover instructions executed by a processor. Not only does such an attack compromise code confidentiality, it can also reveal critical information on the system’s internals. ...

IEEE2022

Decentralized in-order execution of a sequential task-based code for shared-memory architectures

Charly Nicolas Lucien Castes

The hardware complexity of modern machines makes the design of adequate programming models crucial for jointly ensuring performance, portability, and productivity in high-performance computing (HPC). Sequential task-based programming models paired with adv ...

IEEE COMPUTER SOC2022

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Flavio Ponzina

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

2022

In-Memory Hardware and Architectural Extensions for Workloads Acceleration

William Andrew Simon

Utilization of edge devices has exploded in the last decade, with such use cases as wearable devices, autonomous driving, and smart homes. As their ubiquity grows, so do expectations of their capabilities. Simultaneously, their formfactor and use cases lim ...

EPFL2022

Error Resilient In-Memory Computing Architecture for CNN Inference on the Edge

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

The growing popularity of edge computing has fostered the development of diverse solutions to support Artificial Intelligence (AI) in energy-constrained devices. Nonetheless, comparatively few efforts have focused on the resiliency exhibited by AI workload ...

2022

Miss-Optimized Memory Systems: Turning Thousands of Outstanding Misses into Reuse Opportunities

Mikhail Asiatici

Even if Dennard scaling came to an end fifteen years ago, Moore' s law kept fueling an exponential growth in compute performance through increased parallelization. However, the performance of memory and, in particular, Dynamic Random Access Memory (DRAM), ...

EPFL2021

Power Yoga: Variable-Stretch Security of CCM for Energy-Efficient Lightweight IoT

Reza Reyhanitabar, Damian Vizár, Emiljano Gjiriti

The currently ongoing NIST LWC project aims at identifying new standardization targets for lightweight authenticated encryption with associated data (AEAD) and (optionally) lightweight cryptographic hashing. NIST has deemed it important for performance and ...

2021

Admissibility of Uncertain Injections in Quadratic Algebraic Systems

Jean-Yves Le Boudec, Cong Wang, Eleni Stai

We study the admissibility problem in multivariate algebraic systems, such as ac electrical networks, where the power injection is quadratic in the state. The goal of such systems is to ensure that the state stays in some security set (e.g., magnitudes of ...

2021

Towards a secure and trustworthy imaging with non-fungible tokens

Touradj Ebrahimi, Evgeniy Upenik, Davi Nachtigall Lazzarotto

Non fungible tokens (NFTs) are used to define the ownership of digital assets. More recently, there has been a surge of platforms to auction digital art as well as other digital assets in form of image, video, and audio content of all sorts. Although NFTs ...

2021

Atom: A Stream Cipher with Double Key Filter

Willi Meier, Andrea Felice Caforio, Subhadeep Banik

It has been common knowledge that for a stream cipher to be secure against generic TMD tradeoff attacks, the size of its internal state in bits needs to be at least twice the size of the length of its secret key. In FSE 2015, Armknecht and Mikhalev however ...

2021

Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault

Byzantine-resilient Stochastic Gradient Descent (SGD) aims at shielding model training from Byzantine faults, be they ill-labeled training datapoints, exploited software/hardware vulnerabilities, or malicious worker nodes in a distributed setting. Two rece ...

2021

Trust as a Programming Primitive

Adrien Ghosn

Programming has changed; programming languages have not. Modern software embraced reusable software components, i.e., public libraries, and runs in the cloud, on machines that co-locate applications from various origins. This new programming paradigm leads ...

EPFL2021

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