IMC-CLINIC：用于模拟存内计算裁剪的耦合损失信息牛顿迭代

中文摘要 AI 辅助

模拟存内计算（IMC）通过在模拟域内的存储阵列中直接执行矩阵乘法（MatMul），为能效高的大型语言模型（LLM）推理提供了一条有前景的路径。然而，其效率伴随着一个额外的误差来源：有限精度的模数转换器（ADC）对累积的模拟部分和进行量化，引入了与MatMul输入端的传统激活和权重量化不同的输出端误差。裁剪可以减轻操作数和ADC量化误差，但最优裁剪因子必须同时平衡激活舍入与裁剪、权重舍入与裁剪以及ADC量化。现有的裁剪方法专为数字量化设计，并未显式优化这些耦合的IMC误差源，且通常依赖代价高昂的基于搜索的校准。我们提出了IMC-CLINIC（耦合损失信息牛顿迭代裁剪），一种基于IMC MatMul输出误差解析代理的裁剪校准框架。该代理联合建模操作数量化、累积裁剪引起的偏差和ADC量化，使得能够从一个小型校准集高效评估其梯度和近似曲率。IMC-CLINIC使用带保护的牛顿型方法联合优化激活和权重裁剪因子。在多个模型和数据集上，它相比网格搜索基线将平均零样本准确率提高了6.5至11.5个百分点，同时将校准时间减少了10.0至12.1倍。其解析代理紧密跟踪经验IMC输出误差，其优化器在两个代表性模型的所有投影上被认证在损失目标下全局最优的1%以内。

英文摘要

Analog in-memory computing (IMC) offers a promising path toward energy-efficient large language model (LLM) inference by executing matrix multiplications (MatMul) directly within memory arrays in the analog domain. Its efficiency, however, comes with an additional source of error: limited-precision analog-to-digital converters (ADCs) quantize accumulated analog partial sums, introducing output-side error distinct from conventional activation and weight quantization at the MatMul inputs. Clipping can mitigate both operand and ADC quantization errors, but the optimal clipping factors must jointly balance activation rounding and clipping, weight rounding and clipping, and ADC quantization. Existing clipping methods, designed for digital quantization, do not explicitly optimize these coupled sources of IMC error and often rely on costly search-based calibration. We introduce IMC-CLINIC (Coupled Loss-Informed Newton Iterations for Clipping), a clipping calibration framework based on an analytical surrogate for IMC MatMul output error. The surrogate jointly models operand quantization, accumulated clipping-induced bias, and ADC quantization, enabling efficient evaluation of its gradient and approximate curvature from a small calibration set. IMC-CLINIC jointly optimizes activation and weight clipping factors using a safeguarded Newton-type method. Across multiple models and datasets, it improves average zero-shot accuracy by 6.5-11.5 percentage points over the grid search baseline while reducing calibration time by factors of 10.0-12.1. Its analytical surrogate closely tracks empirical IMC output error, and its optimizer is certified within 1% of the global optimum under the loss objective across all projections on two representative models.

发表机构

• Princeton University（普林斯顿大学）
• EnCharge AI

机构由 AI 辅助整理，请以论文原文为准。