MIT 6.004: Computation Structures(6004.mit.edu) |
MIT 6.004: Computation Structures(6004.mit.edu) |
"This subject examines papers every computer scientist should have read, with an emphasis on the period from the 1930s to the 1980s. It is meant to be a synthesizing experience for advanced students in computer science: a way for them to see the field as a whole, not through a survey, but by reliving the experience of its creation, relating the original work to the field as it exists today. The aim is to create a unified view of the field by replaying its entire evolution at an accelerated rate, giving students the opportunity to become sophisticated generalists"
https://www.eecs.mit.edu/academics-admissions/academic-infor...
I mean, I guess most 'computer science' folks today can have a fecund and profitable career and have never heard of these concepts, but... I hope some people still wonder, why do we have clock speeds, and what other alternatives might exist?
https://computationstructures.org/lectures/info/info.html
Also, an archived version of that course is still running on edx ...
https://www.edx.org/course/computation-structures-part-1-dig...
.. where there is a forum available, so people can still ask questions about how to build a 32-bit CPU from scratch using MOSFETs :)
It's also on OCW,
https://ocw.mit.edu/courses/electrical-engineering-and-compu...
Source I have been following this class online since 2015.
It reminds me closely of The Elements of Computing Systems and its companion web-based incarnation Nand2Tetris available at https://www.nand2tetris.org/
Looks like there's been an update since so will take another look...
The 6.004 for 09 has more of the physics than nand2tetris but I can live without that
Verilog and VHDL are serviceable, but I think there is an advantage to having a simple, friendly syntax without the verbosity and overhead of VHDL.
I also liked how Wirth's course involved running on FPGA hardware. It looks like you might be able to do that in the MIT course as well although I didn't see specific labs for it.
You might be interested in the new class: 6.812 Hardware Architecture for Deep Learning.