Basic Research Needs for Microelectronics: Report of the Office of Science Workshop on Basic Research Needs for Microelectronics, October 23 – 25, 2018

Murray, Cherry A.; Guha, Supratik; Reed, Dan; Herrera, Gil; Dam, Kerstin Kleese van; Salahuddin, Sayeef; Ang, James S.; Conte, Thomas M.; Jena, Debdeep; Kaplar, Robert; Atwater, Harry A.; Boroyevich, Dushan; Chappell, William J.; Liu, Tsu‐Jae King; Rattner, Justin; Witherell, M. S.; Afridi, Khurram K.; Ang, Simon S.; Bock, Jonathan A.; Chowdhury, Srabanti; Datta, Suman; Evans, K. R.; Flicker, Jack; Hollis, M.A.; Johnson, N. M.; Jones, K. A.; Kogge, Peter M.; Krishnamoorthy, Sriram; Marinella, Matthew; Monson, Todd C.; Narumanchi, Sreekant; Ohodnicki, Paul R.; Ramesh, R.; Schuette, Michael L.; Shalf, John; Shahedipour-Sandvik, Shadi; Simmons, J. A.; Taylor, Valerie; Theis, Tom; Colby, E.; Pino, Robinson E.; Schwartz, Andy; Runkles, Katie; Harmon, Joseph E.; Nelson, Michele; Skonicki, Vicki

doi:10.2172/1616249

Cited by 8 publications

(12 citation statements)

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“…Due to the size (km-scale) and cost (B$-scale) of the few FEL facilities over the world, the total number of experiments and applications that can be pursued is quite limited, and is prioritized by beam time committees and management priorities. FEL applications in industry (for example, transformative manufacturing [28] and high-volume defect tomography in micro-electronics [29]), at smaller universities (for example for time-resolved nano-scale imaging [30]), and as complementary capability to existing accelerator and light source facilities, could be opened up to users if the size and cost of the FEL facility could be significantly reduced. For this reason, a growing and active global community is exploring advanced accelerator concepts for compact FELs.…”

Section: Free Electron Lasersmentioning

confidence: 99%

Snowmass 2021 Accelerator Frontier White Paper: Near Term Applications driven by Advanced Accelerator Concepts

Emma¹,

Tilborg²,

Albert³

et al. 2022

Preprint

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While the long-term vision of the advanced accelerator community is aimed at addressing the challenges of future collider technology, it is critical that the community takes advantage of the opportunity to make large societal impact through its near-term applications. In turn, enabling robust applications strengthens the quality, control, and reliability of the underlying accelerator infrastructure. The white paper contributions that are solicited here will summarize the near-term applications ideas presented by the advanced accelerator community, assessing their potential impact, discussing scientific and technical readiness of concepts, and providing a timeline for implementation.

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Section: Free Electron Lasersmentioning

confidence: 99%

Snowmass 2021 Accelerator Frontier White Paper: Near Term Applications driven by Advanced Accelerator Concepts

Emma¹,

Tilborg²,

Albert³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Leveraging the commodity semiconductor ecosystem has led to an HPC monoculture, dominated by x86-64 processors and GPU accelerators. Given current semiconductor constraints, substantially increased system performance will require more intentional end-to-end co-design [64], from device physics to applications. Most recently, Japan's collaborative development of the Fugaku supercomputer, based on custom processors with ARM cores and vector instructions [36], demonstrated the power of application-driven end-to-end co-design.…”

Section: A Computing Checkpointmentioning

confidence: 99%

Reinventing High Performance Computing: Challenges and Opportunities

Reed¹,

Gannon²,

Dongarra³

2022

Preprint

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The world of computing is in rapid transition, now dominated by a world of smartphones and cloud services, with profound implications for the future of advanced scientific computing. Simply put, highperformance computing (HPC) is at an important inflection point. For the last 60 years, the world's fastest supercomputers were almost exclusively produced in the United States on behalf of scientific research in the national laboratories. Change is now in the wind. While costs now stretch the limits of U.S. government funding for advanced computing, Japan and China are now leaders in the bespoke HPC systems funded by government mandates. Meanwhile, the global semiconductor shortage and political battles surrounding fabrication facilities affect everyone. However, another, perhaps even deeper, fundamental change has occurred. The major cloud vendors have invested in global networks of massive scale systems that dwarf today's HPC systems. Driven by the computing demands of AI, these cloud systems are increasingly built using custom semiconductors, reducing the financial leverage of traditional computing vendors. These cloud systems are now breaking barriers in game playing and computer vision, reshaping how we think about the nature of scientific computation. Building the next generation of leading edge HPC systems will require rethinking many fundamentals and historical approaches by embracing end-to-end co-design; custom hardware configurations and packaging; large-scale prototyping, as was common thirty years ago; and collaborative partnerships with the dominant computing ecosystem companies, smartphone and cloud computing vendors.

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“…The discrete model (1) can be used to predict how variations in the processor rate affect computer performance, and to understand whether these rates can be controlled to achieve a prescribed objective, such as faster throughput. For the continuum model in (17), such a control is specified via the function α. In this section, we briefly explore the effects of adapting α with the intent of a more extensive study in later work.…”

Section: 3mentioning

confidence: 99%

“…The increasing number and diversity of processing units in modern supercomputers presents a significant challenge to the understanding of how data is globally distributed in these systems. This issue is critically important in emerging computer architectures for which the cost of data movement is becoming a critical driver for system design, affecting hardware and software choices and even basic algorithms [12,17,18]. At the same time, these supercomputers are becoming increasingly difficult to model.…”

mentioning

confidence: 99%