Digital-assisted noise-eliminating training for memristor crossbar-based analog neuromorphic computing engine
Digital-assisted noise-eliminating training for memristor crossbar-based analog neuromorphic computing engine
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DOI：
10.1145/2463209.2488741
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发表时间：
2013-05
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通讯作者：
Beiye Liu;Miao Hu;Hai Helen Li;Zhihong Mao;Yiran Chen;Tingwen Huang;Wei Zhang-
中科院分区：
文献类型：
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作者：
Beiye Liu;Miao Hu;Hai Helen Li;Zhihong Mao;Yiran Chen;Tingwen Huang;Wei Zhang-
神经形态计算体系结构的产生是受到人脑工作机制的启发。忆阻器技术通过在基于忆阻器的交叉开关（MBC）结构上高效地执行模拟矩阵向量乘法来振兴神经形态计算系统设计。然而，由于难以在训练期间实时监测忆阻器状态，将MBC编程为目标状态可能非常具有挑战性。在这项工作中，我们定量分析的MBC编程的工艺变化和输入信号噪声的敏感性。然后，我们在新的交叉结构之上提出了一种降噪训练方法，以最大限度地减少MBC训练期间的噪声积累并提高训练后的系统性能，即，模式回忆率。为了减少训练失败率和训练时间，还引入了MBC训练的数字辅助初始化步骤。实验结果表明，我们的噪声消除训练方法可以提高模式召回率。对于128 × 128像素的测试模式，在相同的模式识别率下，该方法可以减少MBC训练时间12.6% ~ 14.1%，提高模式召回率18.7% ~ 36.2%。
The invention of neuromorphic computing architecture is inspired by the working mechanism of human-brain. Memristor technology revitalized neuromorphic computing system design by efficiently executing the analog Matrix-Vector multiplication on the memristor-based crossbar (MBC) structure. However, programming the MBC to the target state can be very challenging due to the difficulty to real-time monitor the memristor state during the training. In this work, we quantitatively analyzed the sensitivity of the MBC programming to the process variations and input signal noise. We then proposed a noise-eliminating training method on top of a new crossbar structure to minimize the noise accumulation during the MBC training and improve the trained system performance, i.e.,the pattern recall rate. A digital-assisted initialization step for MBC training is also introduced to reduce the training failure rate as well as the training time. Experimental results show that our noise-eliminating training method can improve the pattern recall rate. For the tested patterns with 128 × 128 pixels our technique can reduce the MBC training time by 12.6% ~ 14.1% for the same pattern recognition rate, or improve the pattern recall rate by 18.7% ~ 36.2% for the same training time.