在模拟电阻交叉阵列上训练大规模ANN
Training large-scale ANNs on simulated resistive crossbar arrays
摘要：
使用模拟电阻交叉开关阵列加速人工神经网络（ANN）的训练是一个很有前途的想法。虽然这个概念已经在非常小的ANN和玩具数据集（如MNIST）上得到了验证，但更现实的ANN和数据集尚未得到解决。然而，可以预期的是，器件材料和硬件设计约束，例如噪声计算、器件材料的有限数量的电阻状态、饱和重量和激活范围以及模数转换器的有限精度，将对最先进的ANN的成功训练造成重大挑战。通过使用模拟硬件感知的ANN训练模拟，我们在这里探索了一些简单的算法补偿措施，以应对模拟噪声和有限的权重和输出范围和分辨率，显着提高模拟训练性能的RPU阵列上的中型到大型ANN。
英文摘要：
Accelerating training of artificial neural networks (ANN) with analog resistive crossbar arrays is a promising idea. While the concept has been verified on very small ANNs and toy data sets (such as MNIST), more realistically sized ANNs and datasets have not yet been tackled. However, it is to be expected that device materials and hardware design constraints, such as noisy computations, finite number of resistive states of the device materials, saturating weight and activation ranges, and limited precision of analog-to-digital converters, will cause significant challenges to the successful training of state-of-the-art ANNs. By using analog hardware aware ANN training simulations, we here explore a number of simple algorithmic compensatory measures to cope with analog noise and limited weight and output ranges and resolutions, that dramatically improve the simulated training performances on RPU arrays on intermediately to large-scale ANNs.
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。
