提出新型存内计算数据流，显著降低深度卷积的缓存流量。
Computing-In-Memory Dataflow for Minimal Buffer Traffic
- 通过最大化数据复用提升内存利用率，减少缓存传输
- 在MobileNet和EfficientNet上使数据流量能耗降低10.1%-17.9%
- 适合边缘AI设备中轻量模型的高效部署
存内计算（CIM）通过减少数据搬运缓解内存墙问题，可实现高能效，是边缘AI设备的有前景架构。针对MobileNet和EfficientNet等轻量模型采用深度卷积进行特征提取，但现有CIM宏常面临深度卷积加速难题，包括内存利用率低和缓存流量大。其中，缓存流量对延迟和能耗的影响被长期忽视。为此，本文提出一种新型CIM数据流，通过最大化数据复用和提升内存利用率，显著降低深度卷积中的缓存流量。该数据流基于坚实的理论基础，并在论文中完整验证。应用于MobileNet和EfficientNet时，相比基线（传统权值静态数据流），缓存流量降低77.4%-87.0%，总数据流量能耗减少10.1%-17.9%，延迟降低15.6%-27.8%。
原文摘要 · Abstract (English)
Computing-In-Memory (CIM) offers a potential solution to the memory wall issue and can achieve high energy efficiency by minimizing data movement, making it a promising architecture for edge AI devices. Lightweight models like MobileNet and EfficientNet, which utilize depthwise convolution for feature extraction, have been developed for these devices. However, CIM macros often face challenges in accelerating depthwise convolution, including underutilization of CIM memory and heavy buffer traffic. The latter, in particular, has been overlooked despite its significant impact on latency and energy consumption. To address this, we introduce a novel CIM dataflow that significantly reduces buffer traffic by maximizing data reuse and improving memory utilization during depthwise convolution. The proposed dataflow is grounded in solid theoretical principles, fully demonstrated in this paper. When applied to MobileNet and EfficientNet models, our dataflow reduces buffer traffic by 77.4-87.0%, leading to a total reduction in data traffic energy and latency by 10.1-17.9% and 15.6-27.8%, respectively, compared to the baseline (conventional weight-stationary dataflow).
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