论文标题
教程：具有跨点电阻内存阵列的模拟矩阵计算（AMC）
Tutorial: Analog Matrix Computing (AMC) with Crosspoint Resistive Memory Arrays
论文作者
论文摘要
矩阵计算在现代科学和工程领域无处不在。由于常规数字计算机的计算复杂性很高，因此矩阵计算代表了许多数据密集型应用程序中的重大工作量，例如机器学习，科学计算和无线通信。对于快速，有效的矩阵计算，具有电阻内存阵列的模拟计算已被证明是一个有前途的解决方案。在本教程中，我们根据跨点电阻内存阵列提出模拟矩阵计算（AMC）电路。 AMC电路能够执行基本的矩阵计算，包括矩阵乘法，矩阵倒置，伪内向和特征向量计算，所有这些都具有一个单一操作。我们描述了AMC电路的主要设计原理，例如本地/全局或负面/正反馈配置，具有/没有外部输入。将提出包含负值的矩阵的映射策略。电路稳定性的基本要求将通过传输函数分析来描述，这也定义了电路对稳态结果的时间复杂性。最后，将讨论AMC电路的典型应用，挑战和未来趋势。
Matrix computation is ubiquitous in modern scientific and engineering fields. Due to the high computational complexity in conventional digital computers, matrix computation represents a heavy workload in many data-intensive applications, e.g., machine learning, scientific computing, and wireless communications. For fast, efficient matrix computations, analog computing with resistive memory arrays has been proven to be a promising solution. In this Tutorial, we present analog matrix computing (AMC) circuits based on crosspoint resistive memory arrays. AMC circuits are able to carry out basic matrix computations, including matrix multiplication, matrix inversion, pseudoinverse and eigenvector computation, all with one single operation. We describe the main design principles of the AMC circuits, such as local/global or negative/positive feedback configurations, with/without external inputs. Mapping strategies for matrices containing negative values will be presented. The underlying requirements for circuit stability will be described via the transfer function analysis, which also defines time complexity of the circuits towards steady-state results. Lastly, typical applications, challenges, and future trends of AMC circuits will be discussed.
