227 research outputs found
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Flexible Hardware-based Security-aware Mechanisms and Architectures
For decades, software security has been the primary focus in securing our computing platforms. Hardware was always assumed trusted, and inherently served as the foundation, and thus the root of trust, of our systems. This has been further leveraged in developing hardware-based dedicated security extensions and architectures to protect software from attacks exploiting software vulnerabilities such as memory corruption. However, the recent outbreak of microarchitectural attacks has shaken these long-established trust assumptions in hardware entirely, thereby threatening the security of all of our computing platforms and bringing hardware and microarchitectural security under scrutiny. These attacks have undeniably revealed the grave consequences of hardware/microarchitecture security flaws to the entire platform security, and how they can even subvert the security guarantees promised by dedicated security architectures. Furthermore, they shed light on the sophisticated challenges particular to hardware/microarchitectural security; it is more critical (and more challenging) to extensively analyze the hardware for security flaws prior to production, since hardware, unlike software, cannot be patched/updated once fabricated.
Hardware cannot reliably serve as the root of trust anymore, unless we develop and adopt new design paradigms where security is proactively addressed and scrutinized across the full stack of our computing platforms, at all hardware design and implementation layers. Furthermore, novel flexible security-aware design mechanisms are required to be incorporated in processor microarchitecture and hardware-assisted security architectures, that can practically address the inherent conflict between performance and security by allowing that the trade-off is configured to adapt to the desired requirements.
In this thesis, we investigate the prospects and implications at the intersection of hardware and security that emerge across the full stack of our computing platforms and System-on-Chips (SoCs). On one front, we investigate how we can leverage hardware and its advantages, in contrast to software, to build more efficient and effective security extensions that serve security architectures, e.g., by providing execution attestation and enforcement, to protect the software from attacks exploiting software vulnerabilities. We further propose that they are microarchitecturally configured at runtime to provide different types of security services, thus adapting flexibly to different deployment requirements. On another front, we investigate how we can protect these hardware-assisted security architectures and extensions themselves from microarchitectural and software attacks that exploit design flaws that originate in the hardware, e.g., insecure resource sharing in SoCs. More particularly, we focus in this thesis on cache-based side-channel attacks, where we propose sophisticated cache designs, that fundamentally mitigate these attacks, while still preserving performance by enabling that the performance security trade-off is configured by design. We also investigate how these can be incorporated into flexible and customizable security architectures, thus complementing them to further support a wide spectrum of emerging applications with different performance/security requirements. Lastly, we inspect our computing platforms further beneath the design layer, by scrutinizing how the actual implementation of these mechanisms is yet another potential attack surface. We explore how the security of hardware designs and implementations is currently analyzed prior to fabrication, while shedding light on how state-of-the-art hardware security analysis techniques are fundamentally limited, and the potential for improved and scalable approaches
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023
Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
Operational research:methods and applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
Proof-theoretic Semantics for Intuitionistic Multiplicative Linear Logic
This work is the first exploration of proof-theoretic semantics for a substructural logic. It focuses on the base-extension semantics (B-eS) for intuitionistic multiplicative linear logic (IMLL). The starting point is a review of Sandqvist’s B-eS for intuitionistic propositional logic (IPL), for which we propose an alternative treatment of conjunction that takes the form of the generalized elimination rule for the connective. The resulting semantics is shown to be sound and complete. This motivates our main contribution, a B-eS for IMLL
, in which the definitions of the logical constants all take the form of their elimination rule and for which soundness and completeness are established
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
LIPIcs, Volume 258, SoCG 2023, Complete Volume
LIPIcs, Volume 258, SoCG 2023, Complete Volum
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