交叉点电阻存储阵列的一步回归和分类
One-step regression and classification with crosspoint resistive memory arrays
摘要：
机器学习作为处理日常生活中无处不在的传感器产生的大数据的工具，近年来受到了广泛的关注。需要高速、低能耗的计算机器来实现边缘处的实时人工智能，即，而不需要云中的远程帧服务器的支持。这种要求挑战了互补金属氧化物半导体（CMOS）技术，其受到摩尔定律接近其终点和传统计算架构中的通信瓶颈的限制。因此，迫切需要新的计算概念、架构和设备来加速数据密集型应用。在这里，我们展示了一个交叉点电阻存储器电路与反馈配置可以执行线性回归和逻辑回归在一个步骤中通过计算存储器内的数据的伪逆矩阵。因此，最基本的学习操作，即数据序列的回归和数据集的分类，可以通过新技术在一个单一的计算步骤中执行。一步学习进一步支持的模拟预测的成本在波士顿和2层神经网络的MNIST数字识别的训练。由于交叉点阵列内的物理、并行和模拟计算，所有结果都在一个计算步骤中获得。
英文摘要：
Machine learning has been getting a large attention in the recent years, as a tool to process big data generated by ubiquitous sensors in our daily life. High speed, low energy computing machines are in demand to enable real-time artificial intelligence at the edge, i.e., without the support of a remote frame server in the cloud. Such requirements challenge the complementary metal-oxide-semiconductor (CMOS) technology, which is limited by the Moore's law approaching its end and the communication bottleneck in conventional computing architecture. Novel computing concepts, architectures and devices are thus strongly needed to accelerate data-intensive applications. Here we show a crosspoint resistive memory circuit with feedback configuration can execute linear regression and logistic regression in just one step by computing the pseudoinverse matrix of the data within the memory. The most elementary learning operation, that is the regression of a sequence of data and the classification of a set of data, can thus be executed in one single computational step by the novel technology. One-step learning is further supported by simulations of the prediction of the cost of a house in Boston and the training of a 2-layer neural network for MNIST digit recognition. The results are all obtained in one computational step, thanks to the physical, parallel, and analog computing within the crosspoint array.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
