Leveraging Probabilistic Switching in Superparamagnets for Temporal Information Encoding in Neuromorphic Systems
Leveraging Probabilistic Switching in Superparamagnets for Temporal Information Encoding in Neuromorphic Systems
复制标题
利用超顺磁体中的概率开关进行神经形态系统中的时间信息编码
DOI：
10.1109/tcad.2022.3233926
复制
发表时间：
2022-09
影响因子：
2.9
通讯作者：
Kezhou Yang;Dhuruva Priyan G M;Abhronil Sengupta
中科院分区：
文献类型：
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作者：
Kezhou Yang;Dhuruva Priyan G M;Abhronil Sengupta
脑力启发的计算 - 在解决认知任务中的无与伦比的效率的基础上，出现了一个有前途的途径，可以在当前的神经脑中的研究中逐步研究，这是在深度学习中所面临的几种算法和计算挑战文章，我们认为在概率神经形态系统中采取不同的临时信息的途径可能有助于解决该文章中的某些当前挑战。硬件 - 算法共同设计分析表明，由于临时信息编码，临时信息较高的MNIST数据集上具有较高尖峰的状态压缩三层Spintronics随机尖峰网络的精度为97.41％。
Brain-inspired computing—leveraging neuroscientific principles underpinning the unparalleled efficiency of the brain in solving cognitive tasks—is emerging to be a promising pathway to solve several algorithmic and computational challenges faced by deep learning today. Nonetheless, current research in neuromorphic computing is driven by our well-developed notions of running deep learning algorithms on computing platforms that perform deterministic operations. In this article, we argue that taking a different route of performing temporal information encoding in probabilistic neuromorphic systems may help solve some of the current challenges in the field. The article considers superparamagnetic tunnel junctions as a potential pathway to enable a new generation of brain-inspired computing that combines the facets and associated advantages of two complementary insights from computational neuroscience: 1) how information is encoded and 2) how computing occurs in the brain. The hardware-algorithm co-design analysis demonstrates 97.41% accuracy of a state-compressed 3-layer spintronics-enabled stochastic spiking network on the MNIST dataset with high spiking sparsity due to temporal information encoding.