Enabling the multi-threaded simulation for models written in SystemC

Orientadores: Sandro Rigo, Rodolfo Jardim de AzevedoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: SystemC é uma linguagem de desenvolvimento de sistemas de hardware como, por exemplo, os modelos arquiteturais SoC (Systems-on-Chip) e, em conjunto com a biblioteca e metodologia TLM (Transacüon Levei Modeling), oferece a infraestrutura de simulação necessária capaz de realizar a simulação de software e hardware rapidamente em um alto nível de abstração. O seu núcleo de simulação foi construído como uma cadeia de threads, que são executadas uma por vez. Sendo assim, essa modelagem do núcleo de simulação do SystemC não é capaz de se beneficiar dos recursos oferecidos pelos novos processadores com mais de um núcleo de processamento, para obter ganhos de desempenho de simulação. Com o aumento da complexidade dos projetos de circuitos eletrônicos e a diminuição dos prazos para que um produto de SoC se torne comercial, o desempenho das simulações se tornou essencial. No presente trabalho, apresenta uma nova versão do SystemC capaz de executar em processadores multinúcleos com ganhos de desempenho de 2,üx à 22,029x à versão original em máquinas de 4 e 12 núcleos de processamento simulando plataformas contendo de 4 a 64 threads. Além disso, também foram realizadas mudanças nas interfaces TLM, para que a sincronização dos processos paralelos seja independente dos eventos hoje presentes no SystemC e, devido às alterações no núcleo de simulação do SystemC, a linguagem de descrição de arquitetura ArchC também foi adaptada para conseguir executar em um ambiente paralelo de simulaçãoAbstract: SystemC is a modeling language for development of hardware systems, such SoCs (Systems-on-Chip) architectural models, and integrated with the methodology and library TLM (Transaction Level Modeling), it offers the required simulation platform infrastructure capable to simulate software and hardware in a fast way at different abstration levels. However, its single thread simulation kernel prevents it from utilizing the potential computing power of multi-core machines to speed up the simulation. With the complexity and the functionality of new circuits and applications size increasing and the time-to-market becoming shorter, the simulation speed-up is essential. In the present work, we introduce a new SystemC version, able to perform in multi-core machines and, consequently, with performance gains of 2.Ox to 22.029x to the original version on machines with 4 and 12 cores simulating platforms with 4 to 64 threads. Furthermore, changes were made on the TLM interfaces for parallel process can synchronize independently of SystemC events, and because the changes in the SystemC simulation kernel, Archc also had to be adapted for execute in a parallel simulation environmentMestradoMestre em Ciência da Computaçã

Compiler-centric across-stack deep learning acceleration

Optimizing the deployment of Deep Neural Networks (DNNs) is hard. Despite deep learning approaches increasingly providing state-of-the-art solutions to a variety of difficult problems, such as computer vision and natural language processing, DNNs can be prohibitively expensive, for example, in terms of inference time or memory usage. Effective exploration of the design space requires a holistic approach, including a range of topics from machine learning, systems, and hardware. The rapid proliferation of deep learning applications has raised demand for efficient exploration and acceleration of deep learning based solutions. However, managing the range of optimization techniques, as well as how they interact with each other across the stack is a non-trivial task. A family of emerging specialized compilers for deep learning, tensor compilers, appear to be a strong candidate to help manage the complexity of across-stack optimization choices, and enable new approaches. This thesis presents new techniques and explorations of the Deep Learning Acceleration Stack (DLAS), with the perspective that the tensor compiler will increasingly be the center of this stack. First, we motivate the challenges in exploring DLAS, by describing the experience of running a perturbation study varying parameters at every layer of the stack. The core of the study is implemented using a tensor compiler, which reduces the complexity of evaluating the wide range of variants, although still requires a significant engineering effort to realize. Next, we develop a new algorithm for grouped convolution, a model optimization technique for which existing solutions provided poor inference time scaling. We implement and optimize our algorithm using a tensor compiler, outperforming existing approaches by 5.1× on average (arithmetic mean). Finally, we propose a technique, transfer-tuning, to reduce the search time required for automatic tensor compiler code optimization, reducing the search time required by 6.5× on average. The techniques and contributions of this thesis across these interconnected domains demonstrate the exciting potential of tensor compilers to simplify and improve design space exploration for DNNs, and their deployment. The outcomes of this thesis enable new lines of research to enable machine learning developers to keep up with the rapidly evolving landscape of neural architectures and hardware

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book