arXiv:2607.23390cs.LGmath.OC2026-07

深度可替代精度,但受计算资源和执行方式限制。

When Can Depth Replace Precision? A Resource Theory of Quantized Neural Computation

论文配图:When Can Depth Replace Precision? A Resource Theory of Quantized Neural Computation
图 1 · 摘自论文原文
  • 用纯调度模型分析低比特残差系统的深度与精度关系。
  • 深度增加可逼近极限,但受执行语义影响,误差收敛速率不同。
  • 适合研究量化神经网络资源优化与系统设计的读者。

在固定输入输出映射下,额外的低比特残差计算能否弥补数值精度的缺失?我们将有限深度的量化残差系统建模为从预定义低比特操作库中选择场的纯调度过程,并利用松弛控制刻画其无限深度极限。目标与闭合松弛可达集之间的距离即为结构性下限:对于该操作库,任何深度增加都无法消除此差距。纯调度在有界变差时间依赖下以 $O(D^{-1})$ 速率趋近松弛类,在霍尔德依赖(指数 $ heta$)下为 $O(D^{- heta} + D^{-1})$。执行算术可逆转结论:全状态写回引入 $Dρ_z$ 惩罚并冻结更新,而增量误差反馈将其替换为有界进位项,并满足精确的公共格守恒律。固定教师反例证明该速率紧致:对相干深度-$L$ 一阶高精度比较器,精度匹配需 $D=Θ(L)$。学习码本引入元数据资源,状态依赖路由则引入混合事件条件。验证的原对偶界可在训练前得出可行、不可能或未决结论。配套软件实现工作流,Lean 4 机器检验离散核心。深度替代精度仅相对于声明的操作库、时间范围、执行语义和路由模型而言。

原文摘要 · Abstract (English)

When can additional low-bit residual computation replace missing numerical precision for a fixed input-output map? We model a quantized residual system over a fixed horizon as a pure schedule selecting fields from a declared low-bit operation library, and use relaxed controls to characterize its infinite-depth limit. The distance from the target to the closed relaxed reachable set is the exact structural floor: no increase in depth can remove it for that library. Pure schedules approach the relaxed class at rate $O(D^{-1})$ under bounded-variation time dependence and $O(D^{-\vartheta}+D^{-1})$ under Holder dependence of exponent $\vartheta$. Execution arithmetic can reverse this conclusion: full-state write-back introduces a $Dρ_z$ penalty and can freeze residual updates, whereas increment error feedback replaces this growth by a bounded carry term and obeys an exact common-lattice conservation law. A fixed-teacher converse makes this rate sharp: for coherent depth-$L$ first-order high-precision comparators, accuracy matching requires $D=Θ(L)$. Learned codebooks add a metadata resource, while state-dependent routing introduces hybrid event conditions. Verified primal and dual bounds yield feasible, impossible, or unresolved decisions before training. Companion software implements the workflow, and Lean 4 machine-checks the exact discrete core. Depth replaces precision only relative to a declared library, horizon, execution semantics, and routing model.

量化深度学习资源理论神经网络

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