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opened 2026-03-22 15:34:37 -05:00 by GiteaMirror · 0 comments
GiteaMirror commented 2026-03-22 15:34:37 -05:00
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Originally created by @jasonjabbour on GitHub (Dec 11, 2024).

Originally assigned to: @jasonjabbour on GitHub.

Chapter 2 ML Systems:

  • AI vs ML, Collect all quasi-synonymous terms (TinyML, Edge ML, Edge AI, Embedded AI, Embedded ML, Embedded Intelligence, …) to help readers navigate through the literature
  • Cloud ML: Geopolitical concerns, reliance on foreign powers, reduced sovereignty
  • Edge ML: improve definition to include/exclude telecoms towers. For me the real difference is whether ML runs on computers controlled by one organisation or outsourced?

Chapter 3 DL Primer

  • Traditional ML primer? Agree with main thesis, perhaps the book would be richer if TML is detailed a bit more, e.g. with list of algorithms, pros cons among TML algorithms, and learning resources

Chapter 4 AI Workflow

  • Traditional vs Embedded AI: Highlight that the development and execution of software often occurs on different machines, requiring cross-compilation, flashing, etc, but also making testing/debugging more challenging

Chapter 5 Data Engineering

  • 5.3 Data Sourcing: Foundation: Sensors, ADCs, Sampling Frequency/Resolution, Multimodal sensing, time synchronization
  • 5.4 Data Storage: Anonymization, specific rules derived from ethics approval committees or local laws (GDPR)

Chapter 8 Efficient AI

  • Efficient Numbers: Highlight that sub-8bit quantization needs specialized hardware to be leveraged. A generic CPU may just pad zeros.
  • Efficient Numerics -> Basics: it reads as if integers cannot also be 16, 32, and 64 bits, perhaps it should be rephrased to highlight why a float16 is preferable than an int16
  • Data? Full-System Optimization?
Originally created by @jasonjabbour on GitHub (Dec 11, 2024). Originally assigned to: @jasonjabbour on GitHub. Chapter 2 ML Systems: - [ ] AI vs ML, Collect all quasi-synonymous terms (TinyML, Edge ML, Edge AI, Embedded AI, Embedded ML, Embedded Intelligence, …) to help readers navigate through the literature - [ ] Cloud ML: Geopolitical concerns, reliance on foreign powers, reduced sovereignty - [ ] Edge ML: improve definition to include/exclude telecoms towers. For me the real difference is whether ML runs on computers controlled by one organisation or outsourced? Chapter 3 DL Primer - [ ] Traditional ML primer? Agree with main thesis, perhaps the book would be richer if TML is detailed a bit more, e.g. with list of algorithms, pros cons among TML algorithms, and learning resources Chapter 4 AI Workflow - [ ] Traditional vs Embedded AI: Highlight that the development and execution of software often occurs on different machines, requiring cross-compilation, flashing, etc, but also making testing/debugging more challenging Chapter 5 Data Engineering - [ ] **5.3 Data Sourcing:** Foundation: Sensors, ADCs, Sampling Frequency/Resolution, Multimodal sensing, time synchronization - [ ] **5.4 Data Storage:** Anonymization, specific rules derived from ethics approval committees or local laws (GDPR) Chapter 8 Efficient AI - [ ] Efficient Numbers: Highlight that sub-8bit quantization needs specialized hardware to be leveraged. A generic CPU may just pad zeros. - [ ] Efficient Numerics -> Basics: it reads as if integers cannot also be 16, 32, and 64 bits, perhaps it should be rephrased to highlight why a float16 is preferable than an int16 - [ ] Data? Full-System Optimization?
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