From Computational to Generalized Thinking

Interesting view that I am not quite understanding.   Shall we think as computers, the way the exist today or should we break through from this approach?   Thought provoking, but very technical.

HCDA: From Computational Thinking to a Generalized Thinking Paradigm   By Yuhang Liu, Xian-He Sun, Yang Wang, Yungang Bao

Communications of the ACM, May 2021, Vol. 64 No. 5, Pages 66-75   10.1145/3418291

In 2006, Jeannette M. Wing45 proposed the concept of "computational thinking," which has produced significant worldwide impacts on the education, research, and development of computer science. After more than a decade, we reexamine computational thinking, and find that a more general-thinking paradigm is urgently needed to address new challenges.

A couple of recent commentaries12,41 regarding computational thinking attracted our interests and inspired us to reflect further. More than that, we want to summarize and generalize the rationale of our solutions, for instance, the Labeled von Neumann Architecture (LvNA)1,28 and the Layered Performance Matching (LPM) methodology.26,27

Nurtured by Moore's Law, the number of transistors available on a single chip increases exponentially. Meanwhile, due to architectural innovations, transistors are organized more effectively and utilized more vigorously. As a result of the combined efforts, computers have witnessed a significant performance advancement during their 70-year history. However, the new age, characterized by the slowdown of Moore's Law and Dennard scaling,44 and by the rise of big data applications, brings serious challenges that computer scientists must face.

The scaling of on-chip transistors impacts microprocessor performance significantly. However, further improvements to transistor density and power become more difficult due to the limits of semiconductor physics.44 As a result, architectural innovations become increasingly crucial for performance breakthroughs, and the epoch we are entering is "a new golden age for computer architecture."14

The rise of big data has caused an unprecedented shift, where the memory system, rather than the computational core, plays a more vital role. Accordingly, the memory access limitation described by Sun-Ni's Law38 is becoming a performance killer for many applications. Thus, data-centric innovations of computer system design are urgently needed to address the issues of data storage and access. Both the emergence of big data and the slowdown of Moore's Law have changed the landscape of computer systems and require us to examine past solutions to pave a new path for future innovations and for addressing new challenges.  ... " 

HCDA: ... . A Framework of the Four Thinking Patterns ("H" represents "Historical thinking," "A" represents "Architectural thinking," "D" represents "Data-centric thinking," and "C" represents "Computational thinking"). ... "