论文标题
switchx：gmin-gmax切换可为reram XBARS上的二进制神经网络实施能效和强大的实现
SwitchX: Gmin-Gmax Switching for Energy-Efficient and Robust Implementation of Binary Neural Networks on ReRAM Xbars
论文作者
论文摘要
回忆横梁可以有效地实施二进制神经网络（BNN），其中权重存储在突触的高阻力状态（HRS）和低阻抗状态（LRS）中。我们提出将BNN权重的开关图映射到RERAM横杆上，以使横杆非理想性的影响（导致计算精度降解）被最小化。从本质上讲，SwitchX以这种方式映射二进制重量，使得横杆实例的HR比LRS突触更多。我们发现，与基线的标准横杆映射的BNN相比，BNN映射到具有SwitchX的横杆上，表现出更好的鲁棒性，对对抗性攻击具有更好的鲁棒性。最后，我们将SwitchX与州感知培训（进一步提高了重量映射过程中HRS的可行性）相结合，以提高BNN在硬件上的鲁棒性。我们发现，这种方法比对抗性训练具有更强的防御对抗攻击，这是一种最先进的软件防御。我们使用基准数据集（CIFAR-10，CIFAR-100和Tinyimagenet）对VGG16 BNN进行实验，并使用快速梯度符号方法和预测的梯度下降对抗攻击。我们表明，SwitchX与州感知训练相结合的清洁精度可提高约35％，而针对常规BNN的对抗精度约为6-16％。此外，由于HRS突触的比例增加，SwitchX映射的重要副产品是增加了横杆功率，这是通过州感知的培训进一步的。我们使用CIFAR-10＆CIFAR-100数据集，在16x16＆32x32横梁上通过SwitchX映射的州培训的BNN可为横杆功率消耗节省高达约21-22％。
Memristive crossbars can efficiently implement Binarized Neural Networks (BNNs) wherein the weights are stored in high-resistance states (HRS) and low-resistance states (LRS) of the synapses. We propose SwitchX mapping of BNN weights onto ReRAM crossbars such that the impact of crossbar non-idealities, that lead to degradation in computational accuracy, are minimized. Essentially, SwitchX maps the binary weights in such manner that a crossbar instance comprises of more HRS than LRS synapses. We find BNNs mapped onto crossbars with SwitchX to exhibit better robustness against adversarial attacks than the standard crossbar-mapped BNNs, the baseline. Finally, we combine SwitchX with state-aware training (that further increases the feasibility of HRS states during weight mapping) to boost the robustness of a BNN on hardware. We find that this approach yields stronger defense against adversarial attacks than adversarial training, a state-of-the-art software defense. We perform experiments on a VGG16 BNN with benchmark datasets (CIFAR-10, CIFAR-100 & TinyImagenet) and use Fast Gradient Sign Method and Projected Gradient Descent adversarial attacks. We show that SwitchX combined with state-aware training can yield upto ~35% improvements in clean accuracy and ~6-16% in adversarial accuracies against conventional BNNs. Furthermore, an important by-product of SwitchX mapping is increased crossbar power savings, owing to an increased proportion of HRS synapses, that is furthered with state-aware training. We obtain upto ~21-22% savings in crossbar power consumption for state-aware trained BNN mapped via SwitchX on 16x16 & 32x32 crossbars using the CIFAR-10 & CIFAR-100 datasets.
