论文标题
完全生物化的，平行的，基于RRAM的计算原始计算用于内存相似性搜索
Fully-Binarized, Parallel, RRAM-based Computing Primitive for In-Memory Similarity Search
论文作者
论文摘要
在这项工作中，我们使用RRAM（电阻随机访问存储器）数组提出了一个完全基于XOR的IMS（内存相似性搜索）。 XOR（独家或）操作是使用沿阵列沿列排列的2T-2R位小组实现的。通过执行模拟列的XOR操作和求和来计算HD（Hamming距离），这可以使多个存储的数据向量跨多个存储的数据向量进行匹配。在制造的RRAM阵列上对所提出的方案进行了实验验证。使用开源Skywater 130 nm CMOS PDK通过香料模拟进行全系统验证，使用拟议的BitCell使用拟议的BitCell，其全系统功率耗散为145 $μ$W。可以在固定工作量的情况下使用预计的估算（28 nm）节省$ \ $ 1.5 $ \ times $的能源节省。使用Salinas数据集对HSI（超光谱图像）像素分类任务进行应用程序级验证，证明精度为90％。
In this work, we propose a fully-binarized XOR-based IMSS (In-Memory Similarity Search) using RRAM (Resistive Random Access Memory) arrays. XOR (Exclusive OR) operation is realized using 2T-2R bitcells arranged along the column in an array. This enables simultaneous match operation across multiple stored data vectors by performing analog column-wise XOR operation and summation to compute HD (Hamming Distance). The proposed scheme is experimentally validated on fabricated RRAM arrays. Full-system validation is performed through SPICE simulations using open source Skywater 130 nm CMOS PDK demonstrating energy of 17 fJ per XOR operation using the proposed bitcell with a full-system power dissipation of 145 $μ$W. Using projected estimations at advanced nodes (28 nm) energy savings of $\approx$1.5$\times$ compared to the state-of-the-art can be observed for a fixed workload. Application-level validation is performed on HSI (Hyper-Spectral Image) pixel classification task using the Salinas dataset demonstrating an accuracy of 90%.
