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[PR #1681] [MERGED] fix(book): numerical and clarity corrections across 11 chapter files #24935
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📋 Pull Request Information
Original PR: https://github.com/harvard-edge/cs249r_book/pull/1681
Author: @profvjreddi
Created: 5/5/2026
Status: ✅ Merged
Merged: 5/5/2026
Merged by: @profvjreddi
Base:
dev← Head:fix/numeric-corrections📝 Commits (1)
ff9232afix(book): numerical and clarity corrections across 11 chapter files📊 Changes
11 files changed (+64 additions, -62 deletions)
View changed files
📝
book/quarto/contents/vol1/backmatter/glossary/glossary.qmd(+2 -2)📝
book/quarto/contents/vol1/benchmarking/benchmarking.qmd(+2 -2)📝
book/quarto/contents/vol1/hw_acceleration/hw_acceleration.qmd(+1 -1)📝
book/quarto/contents/vol1/training/training.qmd(+1 -1)📝
book/quarto/contents/vol2/conclusion/conclusion.qmd(+1 -1)📝
book/quarto/contents/vol2/data_storage/data_storage.qmd(+4 -4)📝
book/quarto/contents/vol2/edge_intelligence/edge_intelligence.qmd(+1 -1)📝
book/quarto/contents/vol2/fault_tolerance/fault_tolerance.qmd(+5 -6)📝
book/quarto/contents/vol2/inference/inference.qmd(+15 -8)📝
book/quarto/contents/vol2/ops_scale/ops_scale.qmd(+4 -4)📝
book/quarto/contents/vol2/performance_engineering/performance_engineering.qmd(+28 -32)📄 Description
Summary
Author-initiated factual corrections that had been accumulated as uncommitted edits in the working tree. These tighten numbers, equations, and a few prose sentences. No structural rewrites.
Notable corrections:
training: GPT-2 total FLOPs1e20 → 1e19hw_acceleration: AmdahlH100hw_speedup_factor 500 → 247(H100 INT8 vs ~8 TOPS baseline CPU, more accurate)data_storage: Adam optimizer state700 GB → 1,400 GB(175B params × 2 moments × 4 bytes FP32)edge_intelligence: A11→A17 Pro Neural Engine improvement58× → 27×fault_tolerance: λ20 FITs → 20,000 FITs(×1000 correction);hourly_survival = availability_pct/100(was1 - 1/hours_per_year); LEGO dead-code cleanup (drop unusedhours_str)inference: GPT-3 batch-size table totals recalculated (b8 96.3, b16 row added at 74.4, b32 74.9); prose updated;check()updated to match newLtargetops_scale: cost_savings_pct40% → 57%; capacity_threshold4000 → 2500performance_engineering: B200 dense TFLOPS4500/5000 → 2250(sparse vs dense throughput honesty); ridge points + figures recalculated consistently (139→281 FLOP/byte; 16× → 2× growth in 7 years)glossary: GEMM and ReLU definitions wrap math in LaTeX delimitersbenchmarking: drop unusedV100_FLOPS_FP32importconclusion (vol2): minor prose rewordingVerification:
Test plan
{python}references resolve correctly with new constant values🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.