  This study began with a research project, called DISCvR, conducted at the
IBM-ILLINOIS Center for Cognitive Computing Systems Reseach. The goal of DISCvR
was to build a practical NLP based AI pipeline for document understanding which
will help us better understand the computation patterns and requirements of
modern computing systems. While building such a prototype, an early use case
came to us thanks to the 2017 IEEE/ACM International Symposium on
Microarchitecture (MICRO-50) Program Co-chairs, Drs. Hillery Hunter and Jaime
Moreno. They asked us if we can perform some data-driven analysis of the past
50 years of MICRO papers and show some interesting historical perspectives on
MICRO's 50 years of publication. We learned two important lessons from that
experience: (1) building an AI solution to truly understand unstructured data
is hard in spite of the many claimed successes in natural language
understanding; and (2) providing a data-driven perspective on computer
architecture research is a very interesting and fun project. Recently we
decided to conduct a more thorough study based on all past papers of
International Symposium on Computer Architecture (ISCA) from 1973 to 2018,
which resulted this article. We recognize that we have just scratched the
surface of natural language understanding of unstructured data, and there are
many more aspects that we can improve. But even with our current study, we felt
there were enough interesting findings that may be worthwhile to share with the
community. Hence we decided to write this article to summarize our findings so
far based only on ISCA publications. Our hope is to generate further interests
from the community in this topic, and we welcome collaboration from the
community to deepen our understanding both of the computer architecture
research and of the challenges of NLP-based AI solutions.
