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Technology Prospects for Data-Intensive Computing
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DOI：10.1109/JPROC.2022.3218057.png)
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Abstract
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For many decades, progress in computing hardware has been closely associated with CMOS logic density, performance, and cost. As such, slowdown in 2-D scaling, frequency saturation in CPUs, and increased cost of design and chip fabrication for advanced technology nodes since the early 2000s have led to concerns about how semiconductor technology may evolve in the future. However, the last two decades have also witnessed a parallel development in the application landscape: the advent of big data and consequent rise of data-intensive computing, using techniques such as machine learning. In this article, we advance the idea that data-intensive computing would further cement semiconductor technology as a foundational technology with multidimensional pathways for growth. Continued progress of semiconductor technology in this new context would require the adoption of a system-centric perspective to holistically harness logic, memory, and packaging resources. After examining the performance metrics for data-intensive computing, we present the historical trends for general-purpose graphics processing unit (GPGPU) as a representative data-intensive computing hardware. Thereon, we estimate the values of the key data-intensive computing parameters for the next decade, and our projections may serve as a precursor for a dedicated technology roadmap. By analyzing the compiled data, we identify and discuss specific opportunities and challenges for data-intensive computing hardware technology.
Keywords:
Artificial intelligence (AI)
AI accelerators
big data applications
CMOS technology
deep learning
DRAM chips
energy efficiency
high performance computing
machine learning
Moore's Law
multichip modules (MCMs)
nonvolatile memory
roadmaps (technology planning)
SRAM chips
system integration
system-in-package (SiP)
system-on-chip
three-dimensional integrated circuits
wafer bonding
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