Centre for Neural Engineering, The University of Melbourne, VIC 3010, Australia.
Nanoscale. 2013 Jun 7;5(11):5119-28. doi: 10.1039/c3nr00535f. Epub 2013 May 3.
We report on the implementation of an Associative Capacitive Network (ACN) based on the nondestructive capacitive readout of two Complementary Resistive Switches (2-CRSs). ACNs are capable of performing a fully parallel search for Hamming distances (i.e. similarity) between input and stored templates. Unlike conventional associative memories where charge retention is a key function and hence, they require frequent refresh cycles, in ACNs, information is retained in a nonvolatile resistive state and normal tasks are carried out through capacitive coupling between input and output nodes. Each device consists of two CRS cells and no selective element is needed, therefore, CMOS circuitry is only required in the periphery, for addressing and read-out. Highly parallel processing, nonvolatility, wide interconnectivity and low-energy consumption are significant advantages of ACNs over conventional and emerging associative memories. These characteristics make ACNs one of the promising candidates for applications in memory-intensive and cognitive computing, switches and routers as binary and ternary Content Addressable Memories (CAMs) and intelligent data processing.
我们报告了一种基于非破坏性电容读取的关联电容网络 (ACN) 的实现，该网络基于两个互补电阻开关 (2-CRS)。ACN 能够在输入和存储模板之间执行完全并行的汉明距离 (即相似度) 搜索。与传统的关联存储器不同，电荷保持是关键功能，因此需要频繁的刷新周期，在 ACN 中，信息以非易失性电阻状态保留，并且通过输入和输出节点之间的电容耦合来执行常规任务。每个设备由两个 CRS 单元组成，不需要选择元件，因此，仅在周边需要 CMOS 电路，用于寻址和读出。与传统和新兴的关联存储器相比，高度并行处理、非易失性、广泛的互连性和低能耗是 ACN 的显著优势。这些特性使 ACN 成为应用于内存密集型和认知计算、开关和路由器的二进制和三进制内容可寻址存储器 (CAM) 以及智能数据处理的有前途的候选者之一。
