SPARC 方法：用代表性向量提升光子矩阵乘法推理精度
SPARC: Accurate Photonic Matrix Multiplication via Representative Vectors
光子加速器省电但精度老是上不去，这篇论文的办法挺聪明：相似的激活向量挑一个代表算高精度，剩下的残余交给光子算，噪声影响就小多了。不用改硬件，六个模型上都验证有效，做模拟计算硬件的工程师可以看看。
arXiv 论文《SPARC: Accurate Photonic Matrix Multiplication via Representative Vectors》提出一种提升光子神经网络加速器推理精度的方法。光子加速器通过模拟光硬件执行矩阵乘法来降低 DNN 推理功耗，但精度受限于光子处理所需的低比特量化和模拟光噪声。
该方法利用 DNN 中激活向量之间的高度相似性：为每组相似激活向量构造一个代表性向量，用数字方式以高精度计算其内积，再把残余内积交给光子加速器处理。由于组内激活向量高度相似，残余向量的范数显著更小，从而减轻量化误差与模拟光噪声的影响。该方法保留原有光子内积计算流水线，无需修改硬件即可集成到现有和未来的光子加速器中。
实验在六个代表性 DNN 模型上进行，覆盖多种网络架构和应用领域，均显示推理精度得到改善。对于关注模拟计算硬件与 AI 推理能效的工程团队，这类不依赖硬件改动、只在数值处理层做的优化方案，是可以直接叠加到光子加速器部署上的思路。
SPARC: Accurate Photonic Matrix Multiplication via Representative Vectors
Photonic neural network accelerators have emerged as a promising solution for reducing the power consumption of deep neural network (DNN) inference by executing matrix multiplication on analog photonic hardware. However, their inference accuracy is limited by low-bit quantization required for photonic processing and by analog photonic noise. This paper proposes an approach for improving inference accuracy by exploiting the high similarity among activation vectors in DNNs. Specifically, our approach constructs a representative vector for each group of similar activation vectors, computes its inner product digitally with high precision, and offloads the residual inner products to the photonic accelerator. Since the activation vectors within each group are highly similar, the residual vectors have much smaller norms, thereby reducing the effects of quantization errors and analog photonic noise. Furthermore, our approach preserves the photonic inner-product computation pipeline, allowing seamless integration into existing and future photonic accelerators without hardware modification. Experimental results on six representative DNN models demonstrate that the proposed approach can improve inference accuracy across diverse network architectures and application domains.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include SPARC, 光子计算, 矩阵乘法, 模型推理.
Source attribution: curated from arXiv cs.AI (@Yasuhiro Fujiwara, Tatsuhiro Nakamori, Hideyuki Kawashima).