Preprint / scholarly article

I/O-First Energy Reduction for Transformer-Scale AI

K. Takahashi

Published
DOI
10.5281/zenodo.18015237

Full text PDF (Zenodo)

Abstract

The preprint treats boundary I/O bytes and peak hot memory as auditable budgets, defines an I/O Gain-Shut Kernel that enforces fail-closed admission for transfers and peak usage, and provides certified rewrite rules that guarantee non-increasing boundary cost while preserving meaning or bounded approximation error for transformer-scale workloads.

Keywords

  • AI
  • transformer-scale
  • memory wall
  • I/O governance
  • boundary bytes
  • peak hot memory
  • admission control
  • auditability
  • rewrite rules
  • KV cache
  • activation checkpointing

Identifiers and source records