打造端到端工具链，优化存内计算芯片上二值/三值神经网络推理。
Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer
- 构建模块化工具链，支持从参数评估到编译的全流程设计。
- 实测不同映射方案下模型精度受器件非理想性影响程度。
- 适合存内计算硬件研发人员和神经网络部署工程师使用。
利用阻变存储器（RRAM）交叉阵列实现存内计算（CIM）架构，有望缓解冯·诺依曼瓶颈。由于器件非理想性（如单元波动），RRAM阵列通常仅以二值模式运行，仅使用低阻态（LRS）和高阻态（HRS）。二值神经网络（BNN）和三值神经网络（TNN）因可高效映射至该硬件而尤为适用。现有基于RRAM的CIM软件工具多局限于编译、仿真或设计空间探索（DSE）单一环节，且普遍依赖传统8位量化。为此，本文提出CIM-Explorer，一个面向RRAM交叉阵列上BNN与TNN推理优化的模块化工具包。其包含端到端编译栈、多种映射方式及模拟器，支持跨不同阵列参数与映射策略的精度估计。该工具可贯穿整个设计流程：从特定阵列参数下的早期精度预估，到映射方案选择，再到为最终芯片生成模型。在设计空间探索案例中，验证了不同映射与阵列参数对模型准确率的影响。CIM-Explorer已在GitHub开源。
原文摘要 · Abstract (English)
Using Resistive Random Access Memory (RRAM) crossbars in Computing-in-Memory (CIM) architectures offers a promising solution to overcome the von Neumann bottleneck. Due to non-idealities like cell variability, RRAM crossbars are often operated in binary mode, utilizing only two states: Low Resistive State (LRS) and High Resistive State (HRS). Binary Neural Networks (BNNs) and Ternary Neural Networks (TNNs) are well-suited for this hardware due to their efficient mapping. Existing software projects for RRAM-based CIM typically focus on only one aspect: compilation, simulation, or Design Space Exploration (DSE). Moreover, they often rely on classical 8 bit quantization. To address these limitations, we introduce CIM-Explorer, a modular toolkit for optimizing BNN and TNN inference on RRAM crossbars. CIM-Explorer includes an end-to-end compiler stack, multiple mapping options, and simulators, enabling a DSE flow for accuracy estimation across different crossbar parameters and mappings. CIM-Explorer can accompany the entire design process, from early accuracy estimation for specific crossbar parameters, to selecting an appropriate mapping, and compiling BNNs and TNNs for a finalized crossbar chip. In DSE case studies, we demonstrate the expected accuracy for various mappings and crossbar parameters. CIM-Explorer can be found on GitHub.
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