.png){kind=icon}
返回
Technology Prospects for Data-Intensive Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/JPROC.2022.3218057.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
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
期刊
IF:
25.9
论文数:
9.9K
被引数:
4.5W
机构
引用论文
Postconcussive symptoms in OEF–OIF veterans: Factor structure and impact of posttraumatic stress.Oef-oif退伍军人的脑震荡后症状: 创伤后应激的因素结构和影响。
PREAI
分享收藏The Preparation, Physical Properties, and Structure of Oxotetrachloroaquorhenium(VI), ReOCl4(OH2)
PREAI
分享收藏Direct Prediction of EPR Spectra from Lipid Bilayers: Understanding Structure and Dynamics in Biological Membranes.png)
ChemPhysChem
OAAI
ChemPhysChemIF0
OAAI分享收藏
查看更多内容