ACM Journal on Emerging Technologies in Computing Systems (JETC)

Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19

Epidemiology models are central in understanding and controlling large scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian Computation (ABC) to fit their parameters to observations. ABC inference is highly amenable to efficient hardware acceleration. In this work, we develop parallel ABC inference of a stochastic epidemiology model for COVID-19.

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Architecting for Artificial Intelligence with Emerging Nanotechnology

Artificial Intelligence is becoming ubiquitous in products and services that we use daily. Although the domain of AI has seen substantial improvements over recent years, its effectiveness is limited by the capabilities of current computing technology. Recently, there have been several architectural innovations for AI using emerging nanotechnology. These architectures implement mathematical computations of AI with circuits that utilize physical behavior of nanodevices purpose-built for such computations. This approach leads to a much greater efficiency vs.

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LoGPC: Modeling Network Contention in Message-Passing Programs

In many real applications, for example those with frequent and irregular communication patterns or those using large messages, network contention and contention for message processing resources can be a significant part of the total execution time. This paper presents a new cost model, called LoGPC, that extends the LogP and LogGP models to account for the impact of network contention and network interface DMA behavior on the performance of message-passing programs. We validate LoGPC by analyzing three applications implemented with Active Messages on the MIT Alewife multiprocessor.

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Variability in Nanoscale Fabrics: Bottom-up Integrated Analysis and Mitigation

Emerging nano-device based architectures will be impacted by parameter variation in conjunction with high defect rates. Variations in key physical parameters are caused by manufacturing imprecision as well as fundamental atomic scale randomness. In this paper, the impact of parameter variation on nanoscale computing fabrics is extensively studied through a novel integrated methodology across device, circuit and architectural levels. This integrated approach enables to study in detail the impact of physical parameter variation across all fabric layers.

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