1,645 research outputs found
Language Design for Reactive Systems: On Modal Models, Time, and Object Orientation in Lingua Franca and SCCharts
Reactive systems play a crucial role in the embedded domain. They continuously interact with their environment, handle concurrent operations, and are commonly expected to provide deterministic behavior to enable application in safety-critical systems. In this context, language design is a key aspect, since carefully tailored language constructs can aid in addressing the challenges faced in this domain, as illustrated by the various concurrency models that prevent the known pitfalls of regular threads. Today, many languages exist in this domain and often provide unique characteristics that make them specifically fit for certain use cases. This thesis evolves around two distinctive languages: the actor-oriented polyglot coordination language Lingua Franca and the synchronous statecharts dialect SCCharts. While they take different approaches in providing reactive modeling capabilities, they share clear similarities in their semantics and complement each other in design principles. This thesis analyzes and compares key design aspects in the context of these two languages. For three particularly relevant concepts, it provides and evaluates lean and seamless language extensions that are carefully aligned with the fundamental principles of the underlying language. Specifically, Lingua Franca is extended toward coordinating modal behavior, while SCCharts receives a timed automaton notation with an efficient execution model using dynamic ticks and an extension toward the object-oriented modeling paradigm
A Survey on IEEE 1588 Implementation for RISC-V Low-Power Embedded Devices
IEEE 1588, also known as the Precision Time Protocol (PTP), is a standard protocol for clock synchronization in distributed systems. While it is not architecture-specific, implementing IEEE 1588 on Reduced Instruction Set Computer-V (RISC-V) low-power embedded devices demands considering the system requirements and available resources. This paper explores various approaches and techniques to achieve accurate time synchronization in such instruments. The analysis covers software and hardware implementations, discussing each method’s challenges, benefits, and trade-offs. By examining the state-of-the-art in this field, this paper provides valuable insights and guidance for researchers and engineers working on time-critical applications in RISC-V-based embedded systems, aiding in selecting the most-suitable stack for their designs.This work was partially supported by the ECSEL Joint Undertaking in the H2020 project IMOCO4.E, grant agreement No.10100731, and by the Basque Government within the fund for research groups of the Basque University System IT1440-22 and KK-2023/00015
Tools for efficient Deep Learning
In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption.
We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work.
This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C.
Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets.
All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces
Towards trustworthy computing on untrustworthy hardware
Historically, hardware was thought to be inherently secure and trusted due to its
obscurity and the isolated nature of its design and manufacturing. In the last two
decades, however, hardware trust and security have emerged as pressing issues.
Modern day hardware is surrounded by threats manifested mainly in undesired
modifications by untrusted parties in its supply chain, unauthorized and pirated
selling, injected faults, and system and microarchitectural level attacks. These threats,
if realized, are expected to push hardware to abnormal and unexpected behaviour
causing real-life damage and significantly undermining our trust in the electronic and
computing systems we use in our daily lives and in safety critical applications. A
large number of detective and preventive countermeasures have been proposed in
literature. It is a fact, however, that our knowledge of potential consequences to
real-life threats to hardware trust is lacking given the limited number of real-life
reports and the plethora of ways in which hardware trust could be undermined. With
this in mind, run-time monitoring of hardware combined with active mitigation of
attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed
as the last line of defence. This last line of defence allows us to face the issue of live
hardware mistrust rather than turning a blind eye to it or being helpless once it occurs.
This thesis proposes three different frameworks towards trustworthy computing
on untrustworthy hardware. The presented frameworks are adaptable to different
applications, independent of the design of the monitored elements, based on
autonomous security elements, and are computationally lightweight. The first
framework is concerned with explicit violations and breaches of trust at run-time,
with an untrustworthy on-chip communication interconnect presented as a potential
offender. The framework is based on the guiding principles of component guarding,
data tagging, and event verification. The second framework targets hardware elements
with inherently variable and unpredictable operational latency and proposes a
machine-learning based characterization of these latencies to infer undesired latency
extensions or denial of service attacks. The framework is implemented on a DDR3
DRAM after showing its vulnerability to obscured latency extension attacks. The
third framework studies the possibility of the deployment of untrustworthy hardware
elements in the analog front end, and the consequent integrity issues that might arise
at the analog-digital boundary of system on chips. The framework uses machine
learning methods and the unique temporal and arithmetic features of signals at this
boundary to monitor their integrity and assess their trust level
Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches
Traditional networking devices support only fixed features and limited configurability.
Network softwarization leverages programmable software and hardware platforms to remove those limitations.
In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms.
This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0.
P4 is the most popular technology to implement programmable data planes.
However, programmable data planes, and in particular, the P4 technology, emerged only recently.
Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking.
The research of this thesis focuses on two open issues of programmable data planes.
First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet.
Second, it enables BIER in high-performance P4 data planes.
BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet.
The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study.
Two more peer-reviewed papers contain additional content that is not directly related to the main results.
They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts
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Search for exotic Higgs boson decay to multiple b-quarks with the ATLAS detector at LHC using machine learning methods
The discovery of the Higgs boson has opened up new possibilities for investigating physics beyond the Standard Model (SM). New particles may interact with the SM through the Higgs boson, and deviations from SM predictions can indicate the presence of new physics. This dissertation focuses on the search for exotic Higgs decay, H\rightarrow aa \rightarrow (b\bbar)(b\bbar) where a is a new scalar boson and focuses on the case where the Higgs boson is produced in association with a Z boson. The data were collected by the ATLAS detector at the Large Hadron Collider at center-of-mass energy \sqrt{s} =~13~\TeV from 2015 to 2018 with a total integrated luminosity of 140 fb^{-1}. A new general-purpose low-mass double-b tagging algorithm was developed and applied in the search. With new machine-learning reconstruction techniques, the search is able to set a strong limit on the branching ratio of Br(H\rightarrow aa \rightarrow (b\bbar)(b\bbar)). Furthermore, the search also considers models with two new scalars of different masses, and , and sets the limits on the branching ratio of Br(H\rightarrow a_{1}a_{2} \rightarrow (b\bbar)(b\bbar)
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