561 research outputs found
A Survey of FPGA Optimization Methods for Data Center Energy Efficiency
This article provides a survey of academic literature about field
programmable gate array (FPGA) and their utilization for energy efficiency
acceleration in data centers. The goal is to critically present the existing
FPGA energy optimization techniques and discuss how they can be applied to such
systems. To do so, the article explores current energy trends and their
projection to the future with particular attention to the requirements set out
by the European Code of Conduct for Data Center Energy Efficiency. The article
then proposes a complete analysis of over ten years of research in energy
optimization techniques, classifying them by purpose, method of application,
and impacts on the sources of consumption. Finally, we conclude with the
challenges and possible innovations we expect for this sector.Comment: Accepted for publication in IEEE Transactions on Sustainable
Computin
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
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Improving low latency applications for reconfigurable devices
This thesis seeks to improve low latency application performance via architectural improvements in reconfigurable devices. This is achieved by improving resource utilisation and access, and by exploiting the different environments within which reconfigurable devices are deployed.
Our first contribution leverages devices deployed at the network level to enable the low latency processing of financial market data feeds. Financial exchanges transmit messages via two identical data feeds to reduce the chance of message loss. We present an approach to arbitrate these redundant feeds at the network level using a Field-Programmable Gate Array (FPGA). With support for any messaging protocol, we evaluate our design using the NASDAQ TotalView-ITCH, OPRA, and ARCA data feed protocols, and provide two simultaneous outputs: one prioritising low latency, and one prioritising high reliability with three dynamically configurable windowing methods.
Our second contribution is a new ring-based architecture for low latency, parallel access to FPGA memory. Traditional FPGA memory is formed by grouping block memories (BRAMs) together and accessing them as a single device. Our architecture accesses these BRAMs independently and in parallel. Targeting memory-based computing, which stores pre-computed function results in memory, we benefit low latency applications that rely on: highly-complex functions; iterative computation; or many parallel accesses to a shared resource. We assess square root, power, trigonometric, and hyperbolic functions within the FPGA, and provide a tool to convert Python functions to our new architecture.
Our third contribution extends the ring-based architecture to support any FPGA processing element. We unify E heterogeneous processing elements within compute pools, with each element implementing the same function, and the pool serving D parallel function calls. Our implementation-agnostic approach supports processing elements with different latencies, implementations, and pipeline lengths, as well as non-deterministic latencies. Compute pools evenly balance access to processing elements across the entire application, and are evaluated by implementing eight different neural network activation functions within an FPGA.Open Acces
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
SoC-based FPGA architecture for image analysis and other highly demanding applications
Al giorno d'oggi, lo sviluppo di algoritmi si concentra su calcoli efficienti in termini di prestazioni ed efficienza energetica. Tecnologie come il field programmable gate array (FPGA) e il system on chip (SoC) basato su FPGA (FPGA/SoC) hanno dimostrato la loro capacitĂ di accelerare applicazioni di calcolo intensive risparmiando al contempo il consumo energetico, grazie alla loro capacitĂ di elevato parallelismo e riconfigurazione dell'architettura.
Attualmente, i cicli di progettazione esistenti per FPGA/SoC sono lunghi, a causa della complessitĂ dell'architettura. Pertanto, per colmare il divario tra le applicazioni e le architetture FPGA/SoC e ottenere un design hardware efficiente per l'analisi delle immagini e altri applicazioni altamente demandanti utilizzando lo strumento di sintesi di alto livello, vengono prese in considerazione due strategie complementari: tecniche ad hoc e stima delle prestazioni.
Per quanto riguarda le tecniche ad-hoc, tre applicazioni molto impegnative sono state accelerate attraverso gli strumenti HLS: discriminatore di forme di impulso per i raggi cosmici, classificazione automatica degli insetti e re-ranking per il recupero delle informazioni, sottolineando i vantaggi quando questo tipo di applicazioni viene attraversato da tecniche di compressione durante il targeting dispositivi FPGA/SoC.
Inoltre, in questa tesi viene proposto uno stimatore delle prestazioni per l'accelerazione hardware per prevedere efficacemente l'utilizzo delle risorse e la latenza per FPGA/SoC, costruendo un ponte tra l'applicazione e i domini architetturali. Lo strumento integra modelli analitici per la previsione delle prestazioni e un motore design space explorer (DSE) per fornire approfondimenti di alto livello agli sviluppatori di hardware, composto da due motori indipendenti: DSE basato sull'ottimizzazione a singolo obiettivo e DSE basato sull'ottimizzazione evolutiva multiobiettivo.Nowadays, the development of algorithms focuses on performance-efficient and energy-efficient computations. Technologies such as field programmable gate array (FPGA) and system on chip (SoC) based on FPGA (FPGA/SoC) have shown their ability to accelerate intensive computing applications while saving power consumption, owing to their capability of high parallelism and reconfiguration of the architecture.
Currently, the existing design cycles for FPGA/SoC are time-consuming, owing to the complexity of the architecture. Therefore, to address the gap between applications and FPGA/SoC architectures and to obtain an efficient hardware design for image analysis and highly demanding applications using the high-level synthesis tool, two complementary strategies are considered: ad-hoc techniques and performance estimator.
Regarding ad-hoc techniques, three highly demanding applications were accelerated through HLS tools: pulse shape discriminator for cosmic rays, automatic pest classification, and re-ranking for information retrieval, emphasizing the benefits when this type of applications are traversed by compression techniques when targeting FPGA/SoC devices.
Furthermore, a comprehensive performance estimator for hardware acceleration is proposed in this thesis to effectively predict the resource utilization and latency for FPGA/SoC, building a bridge between the application and architectural domains. The tool integrates analytical models for performance prediction, and a design space explorer (DSE) engine for providing high-level insights to hardware developers, composed of two independent sub-engines: DSE based on single-objective optimization and DSE based on evolutionary multi-objective optimization
On-premise containerized, light-weight software solutions for Biomedicine
Bioinformatics software systems are critical tools for analysing large-scale biological
data, but their design and implementation can be challenging due to the need for reliability, scalability, and performance. This thesis investigates the impact of several
software approaches on the design and implementation of bioinformatics software
systems. These approaches include software patterns, microservices, distributed
computing, containerisation and container orchestration. The research focuses on
understanding how these techniques affect bioinformatics software systems’ reliability, scalability, performance, and efficiency. Furthermore, this research highlights
the challenges and considerations involved in their implementation. This study also
examines potential solutions for implementing container orchestration in bioinformatics research teams with limited resources and the challenges of using container
orchestration. Additionally, the thesis considers microservices and distributed computing and how these can be optimised in the design and implementation process to
enhance the productivity and performance of bioinformatics software systems. The
research was conducted using a combination of software development, experimentation, and evaluation. The results show that implementing software patterns can
significantly improve the code accessibility and structure of bioinformatics software
systems. Specifically, microservices and containerisation also enhanced system reliability, scalability, and performance. Additionally, the study indicates that adopting
advanced software engineering practices, such as model-driven design and container
orchestration, can facilitate efficient and productive deployment and management of
bioinformatics software systems, even for researchers with limited resources. Overall, we develop a software system integrating all our findings. Our proposed system
demonstrated the ability to address challenges in bioinformatics. The thesis makes
several key contributions in addressing the research questions surrounding the design,
implementation, and optimisation of bioinformatics software systems using software
patterns, microservices, containerisation, and advanced software engineering principles and practices. Our findings suggest that incorporating these technologies can
significantly improve bioinformatics software systems’ reliability, scalability, performance, efficiency, and productivity.Bioinformatische Software-Systeme stellen bedeutende Werkzeuge für die Analyse
umfangreicher biologischer Daten dar. Ihre Entwicklung und Implementierung kann
jedoch aufgrund der erforderlichen Zuverlässigkeit, Skalierbarkeit und Leistungsfähigkeit eine Herausforderung darstellen. Das Ziel dieser Arbeit ist es, die Auswirkungen von Software-Mustern, Microservices, verteilten Systemen, Containerisierung
und Container-Orchestrierung auf die Architektur und Implementierung von bioinformatischen Software-Systemen zu untersuchen. Die Forschung konzentriert sich
darauf, zu verstehen, wie sich diese Techniken auf die Zuverlässigkeit, Skalierbarkeit,
Leistungsfähigkeit und Effizienz von bioinformatischen Software-Systemen auswirken
und welche Herausforderungen mit ihrer Konzeptualisierungen und Implementierung
verbunden sind. Diese Arbeit untersucht auch potenzielle Lösungen zur Implementierung von Container-Orchestrierung in bioinformatischen Forschungsteams mit begrenzten Ressourcen und die Einschränkungen bei deren Verwendung in diesem Kontext. Des Weiteren werden die Schlüsselfaktoren, die den Erfolg von bioinformatischen Software-Systemen mit Containerisierung, Microservices und verteiltem Computing beeinflussen, untersucht und wie diese im Design- und Implementierungsprozess optimiert werden können, um die Produktivität und Leistung bioinformatischer
Software-Systeme zu steigern. Die vorliegende Arbeit wurde mittels einer Kombination aus Software-Entwicklung, Experimenten und Evaluation durchgefĂĽhrt. Die
erzielten Ergebnisse zeigen, dass die Implementierung von Software-Mustern, die Zuverlässigkeit und Skalierbarkeit von bioinformatischen Software-Systemen erheblich
verbessern kann. Der Einsatz von Microservices und Containerisierung trug ebenfalls zur Steigerung der Zuverlässigkeit, Skalierbarkeit und Leistungsfähigkeit des
Systems bei. DarĂĽber hinaus legt die Arbeit dar, dass die Anwendung von SoftwareEngineering-Praktiken, wie modellgesteuertem Design und Container-Orchestrierung,
die effiziente und produktive Bereitstellung und Verwaltung von bioinformatischen
Software-Systemen erleichtern kann. Zudem löst die Implementierung dieses SoftwareSystems, Herausforderungen für Forschungsgruppen mit begrenzten Ressourcen. Insgesamt hat das System gezeigt, dass es in der Lage ist, Herausforderungen im Bereich
der Bioinformatik zu bewältigen und stellt somit ein wertvolles Werkzeug für Forscher in diesem Bereich dar. Die vorliegende Arbeit leistet mehrere wichtige Beiträge
zur Beantwortung von Forschungsfragen im Zusammenhang mit dem Entwurf, der
Implementierung und der Optimierung von Software-Systemen fĂĽr die Bioinformatik unter Verwendung von Prinzipien und Praktiken der Softwaretechnik. Unsere
Ergebnisse deuten darauf hin, dass die Einbindung dieser Technologien die Zuverlässigkeit, Skalierbarkeit, Leistungsfähigkeit, Effizienz und Produktivität bioinformatischer Software-Systeme erheblich verbessern kann
AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems
The evolution towards 6G architecture promises a transformative shift in
communication networks, with artificial intelligence (AI) playing a pivotal
role. This paper delves deep into the seamless integration of Large Language
Models (LLMs) and Generalized Pretrained Transformers (GPT) within 6G systems.
Their ability to grasp intent, strategize, and execute intricate commands will
be pivotal in redefining network functionalities and interactions. Central to
this is the AI Interconnect framework, intricately woven to facilitate
AI-centric operations within the network. Building on the continuously evolving
current state-of-the-art, we present a new architectural perspective for the
upcoming generation of mobile networks. Here, LLMs and GPTs will
collaboratively take center stage alongside traditional pre-generative AI and
machine learning (ML) algorithms. This union promises a novel confluence of the
old and new, melding tried-and-tested methods with transformative AI
technologies. Along with providing a conceptual overview of this evolution, we
delve into the nuances of practical applications arising from such an
integration. Through this paper, we envisage a symbiotic integration where AI
becomes the cornerstone of the next-generation communication paradigm, offering
insights into the structural and functional facets of an AI-native 6G network
Throughput and Memory Optimization for Parallel Implementations of Dataflow Networks Using Multi-Reader Buffers
In this paper, we introduce the concept of Multi-Reader Buffers (MRBs) for high throughput and memory-efficient implementation of dataflow applications. Our work is motivated by the huge amount of data that needs to be processed and typically accessed in a FIFO manner, particularly in image and video processing applications. Here, multi-cast, fork, and merge operator implementations known today produce huge memory overheads by storing and communicating copies of the same data. As a remedy, we first introduce MRBs as buffers preserving FIFO semantics for a finite number of readers of the same data while storing each data item only once. Second, we present an approach for memory minimization of data flow networks by replacing all multi-cast actors and connected FIFOs with MRBs. Third, we present a Design Space Exploration approach to selectively replace multi-cast actors with MRBs in order to explore memory, throughput, and processor resource allocation tradeoffs. Our results show that the explored Pareto fronts of our approach improve the solution quality over a reference by 78% in average for six benchmark applications in terms of a hypervolume indicator
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