Uncore RPD: Rapid design space exploration of the uncore via regression modeling

Sangaiah, Karthik; Hempstead, Mark; Taşkın, Barış

doi:10.1109/iccad.2015.7372593

Cited by 6 publications

(11 citation statements)

References 20 publications

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“…ESP [164] constructs the regression model with elastic-net regularization to predict application interference (i.e., slowdown), which is integrated with schedulers to increase throughput. In consideration of rapid design space exploration of the uncore (i.e., both memory hierarchies and NoCs), Sangaiah et al [196] uses a regression-based model with restricted cubic splines to estimate the CPI of CMP, reducing the exploration time by up to four orders of magnitude.…”

Section: General Modelling and Performance Predictionmentioning

confidence: 99%

A Survey of Machine Learning for Computer Architecture and Systems

Wu,

Xie

2021

Preprint

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It has been a long time that computer architecture and systems are optimized to enable efficient execution of machine learning (ML) algorithms or models. Now, it is time to reconsider the relationship between ML and systems, and let ML transform the way that computer architecture and systems are designed. This embraces a twofold meaning: the improvement of designers' productivity, and the completion of the virtuous cycle. In this paper, we present a comprehensive review of work that applies ML for system design, which can be grouped into two major categories, ML-based modelling that involves predictions of performance metrics or some other criteria of interest, and ML-based design methodology that directly leverages ML as the design tool. For ML-based modelling, we discuss existing studies based on their target level of system, ranging from the circuit level to the architecture/system level. For ML-based design methodology, we follow a bottom-up path to review current work, with a scope of (micro-)architecture design (memory, branch prediction, NoC), coordination between architecture/system and workload (resource allocation and management, data center management, and security), compiler, and design automation. We further provide a future vision of opportunities and potential directions, and envision that applying ML for computer architecture and systems would thrive in the community. CCS Concepts: • Computing methodologies → Machine learning; • Computer systems organization → Architectures; • General and reference → Surveys and overviews.

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Section: General Modelling and Performance Predictionmentioning

confidence: 99%

A Survey of Machine Learning for Computer Architecture and Systems

Wu,

Xie

2021

Preprint

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“…Another obtained result was the reduction in simulation time, from 1370 years (using gem5) to 180 hours (using the proposed work). However, they did not cite which NoC attributes were optimized (Sangaiah et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Uma Ferramenta Baseada Em Inteligência Artificial Para Exploração De Espaço De Projeto Para Redes Em Chip / an Artificial Intelligence-Based Tool for Exploiting Design Space for Chip Networks

Silva¹,

Kreutz²,

Pereira³

2021

BJD

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Com o incremento em números de núcleos em sistemas em chip, arquiteturas de barramento tem sofrido com algumas limitações. Os requisitos das aplicações demandam mais largura de banda e baixas latências. Em face a esse cenário, redes em chip emergiram como uma opção para superar essas limitações. Redes em chip são compostas por um conjunto de roteadores e enlaces de comunicação. Nesse trabalho, nós propomos o uso de técnicas de inteligência artificial para otimizar a arquitetura das redes em chip. A ferramenta explora o espaço de projeto em termos de predição de área, latência e potência para diferentes configurações. Os resultados têm demonstrado a validade dessa proposta e a adequação as restrições impostas pelo projetista.Palavras-chave: Redes em chip, inteligência artificial, sistemas em chip.

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“…Evaluation showed lower error (3% error vs 10% error) than an existing analytical approach. Sangaiah et al [67] considered both NoC and memory configuration for performance prediction and design space exploration. Following a standard approach, they sampled a small portion of the design space, then trained a regression model to predict the resulting system CPI.…”

Section: Admission and Flow Controlmentioning

confidence: 99%

“…Parallel threads are also utilized to scale design space exploration with increasing computational resources. System-level design space exploration has favored more standard supervised learning approaches [17], [64], [67]. Specific model choices vary, with linear [17], [64] and non-linear [67] regression models, as well as random forests and neural networks [64] finding implementation.…”

Section: Offline ML Applicationsmentioning

confidence: 99%

“…System-level design space exploration has favored more standard supervised learning approaches [17], [64], [67]. Specific model choices vary, with linear [17], [64] and non-linear [67] regression models, as well as random forests and neural networks [64] finding implementation. As in online ML applications, discussed in Section 4.1, some tasks are naturally limited to supervised learning methods.…”

Section: Offline ML Applicationsmentioning

confidence: 99%

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A Survey of Machine Learning Applied to Computer Architecture Design

Penney¹,

Chen

2019

Preprint

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Machine learning has enabled significant benefits in diverse fields, but, with a few exceptions, has had limited impact on computer architecture. Recent work, however, has explored broader applicability for design, optimization, and simulation. Notably, machine learning based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This paper reviews machine learning applied system-wide to simulation and run-time optimization, and in many individual components, including memory systems, branch predictors, networks-on-chip, and GPUs. The paper further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated architectural design.

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Uncore RPD: Rapid design space exploration of the uncore via regression modeling

Cited by 6 publications

References 20 publications

A Survey of Machine Learning for Computer Architecture and Systems

A Survey of Machine Learning for Computer Architecture and Systems

Uma Ferramenta Baseada Em Inteligência Artificial Para Exploração De Espaço De Projeto Para Redes Em Chip / an Artificial Intelligence-Based Tool for Exploiting Design Space for Chip Networks

A Survey of Machine Learning Applied to Computer Architecture Design

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