论文标题
设备的非理想弹性方法，用于映射神经网络横杆阵列
A Device Non-Ideality Resilient Approach for Mapping Neural Networks to Crossbar Arrays
论文作者
论文摘要
我们提出了一种与技术无关的方法，称为相邻连接矩阵（ACM），以有效地将签名的权重矩阵映射到非阴性横梁阵列。与相同的跨越映射方法相比，使用ACM使用ACM可通过5位精度横杆阵列或更低的训练时，使用CIFAR-10数据集改善Resnet-20的训练准确性高达20％。与使用两个元素代表重量的策略相比，ACM可以达到可比的训练精确度，同时提供区域并分别读取2.3倍和7倍的能量。 ACM还具有轻度的正则化效果，可提高横杆阵列中的推理精度，而无需进行任何重新训练或昂贵的设备/变异感知训练。
We propose a technology-independent method, referred to as adjacent connection matrix (ACM), to efficiently map signed weight matrices to non-negative crossbar arrays. When compared to same-hardware-overhead mapping methods, using ACM leads to improvements of up to 20% in training accuracy for ResNet-20 with the CIFAR-10 dataset when training with 5-bit precision crossbar arrays or lower. When compared with strategies that use two elements to represent a weight, ACM achieves comparable training accuracies, while also offering area and read energy reductions of 2.3x and 7x, respectively. ACM also has a mild regularization effect that improves inference accuracy in crossbar arrays without any retraining or costly device/variation-aware training.
