Wang Wei, Song Wenhao, Yao Peng, Li Yang, Van Nostrand Joseph, Qiu Qinru, Ielmini Daniele, Yang J Joshua
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza L. da Vinci 32, Milano 20133, Italy.
Electrical and Computer Engineering Department, University of Southern California, Los Angeles, CA, USA.
iScience. 2020 Nov 17;23(12):101809. doi: 10.1016/j.isci.2020.101809. eCollection 2020 Dec 18.
Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic computing a promising technology for future artificial intelligence. However, these similarities do not directly transfer to the success of efficient computation without device and algorithm co-designs and optimizations. Contemporary deep learning algorithms demand the memristive artificial synapses to ideally possess analog weighting and linear weight-update behavior, requiring substantial device-level and circuit-level optimization. Such co-design and optimization have been the main focus of memristive neuromorphic engineering, which often abandons the "non-ideal" behaviors of memristive devices, although many of them resemble what have been observed in biological components. Novel brain-inspired algorithms are being proposed to utilize such behaviors as unique features to further enhance the efficiency and intelligence of neuromorphic computing, which calls for collaborations among electrical engineers, computing scientists, and neuroscientists.
忆阻器件在物理机制层面和单元功能层面与生物突触、树突和神经元有着显著的相似性，这使得忆阻式神经形态计算方法成为未来人工智能的一项有前景的技术。然而，如果没有器件和算法的协同设计与优化，这些相似性并不能直接转化为高效计算的成功。当代深度学习算法要求忆阻式人工突触理想地具备模拟加权和线性权重更新行为，这需要大量的器件级和电路级优化。这种协同设计和优化一直是忆阻式神经形态工程的主要焦点，该工程通常会摒弃忆阻器件的“非理想”行为，尽管其中许多行为与在生物组件中观察到的行为相似。正在提出新颖的受脑启发的算法，以利用这些行为作为独特特征来进一步提高神经形态计算的效率和智能，这需要电气工程师、计算科学家和神经科学家之间的合作。
