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[GH-ISSUE #947] Mlsysbook_chapter_3_3&table18.1 #1643
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Reference: github-starred/cs249r_book#1643
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Originally created by @formlsysbookissue on GitHub (Aug 22, 2025).
Original GitHub issue: https://github.com/harvard-edge/cs249r_book/issues/947
Originally assigned to: @profvjreddi on GitHub.
The current analogy states that dendrites/synapses correspond to weights in DNNs. However, a more accurate mapping is: Inputs correspond to dendrites — because dendrites receive incoming signals (inputs). Weights correspond to synapses — because synapses modulate or scale the strength of signals between neurons. Clarifying this helps avoid confusion when relating biological neural structures to artificial neural networks. The biological role of synapses as modulators aligns better with the concept of weights in DNNs, whereas dendrites serve primarily as signal receivers (analogy; a antenna).
Another minor improvement suggestion: Optical interconnects are mentioned in Table 18.1 with their related materials (also in ch 11.9 but just as a pass), but given their growing importance, it would be helpful to include a short section or an explanation box highlighting this. Optical interconnects are becoming increasingly critical due to their advantages like high bandwidth, low latency, and reduced electromagnetic interference. A brief note on their applications in data centers and AI hardware would add valuable context.
@profvjreddi commented on GitHub (Aug 22, 2025):
Thanks! Really appreciate this. I will make a pass on your feedback soon.
@profvjreddi commented on GitHub (Aug 25, 2025):
@formlsysbookissue Thanks again for raising this! I’ve closed the issue since I’ve updated the content. You can review the latest merged version here: https://harvard-edge.github.io/cs249r_book_dev/
(note: this is the DEV version, not generally public).
If you’d like, I’d be happy to tag you on future edits to your suggestions before committing so that you can tell me if my fixes address your questions/concerns, but I didn’t want to assume on your time without checking first.
Feedback on whether this revision addresses your concern would be very welcome.
@formlsysbookissue commented on GitHub (Sep 4, 2025):
Dear Prof. Reddi,
Thank you very much for your prompt response and the thoughtful revisions you’ve made to the content. After reviewing the last updated version, I believe the changes effectively address my concerns.
Beyond the book itself, this experience has been incredibly exciting. Although our time working together has been relatively short, both working on the book and collaborating with you have been motivating and rewarding.
I would be honored to contribute to future projects as well, should the opportunity arise. I would greatly appreciate it if you could tag me for any further edits or revisions as needed.
Sincerely
@profvjreddi commented on GitHub (Sep 4, 2025):
Awesome! 🤝 Thank you for your kind words and for taking the time to review the updates. I’ll certainly keep you in mind for any future edits or revisions. Thank you again for your contributions to the book!