20191031日,来自美国IBM 公司Abu Sebastian研究员应邀来访我基地,并在微纳电子大厦205会议室做了题为Brain-inspired computing at multiple levels of inspiration: A memory device perspective的学术报告。微纳电子学系多位老师以及研究生、本科生参加了报告会,并与报告人进行了热烈的讨论。

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Brain-inspired computing at multiple levels of inspiration: A memory device perspective

 There is a significant need to build efficient non-von Neumann computing systems for highly data-centric artificial intelligence related applications. Brain-inspired computing is one such approach that shows significant promise. Memory is expected to play a key role in this form of computing and in particular, phase-change memory (PCM), arguably the most advanced emerging non-volatile memory technology. Brain-inspired computing is likely to be realized in multiple levels of inspiration given a lack of comprehensive understanding of the working principles of the brain. In the first level of inspiration, the idea would be to build computing units where memory and processing co-exist in some form. Computational memory is an example where the physical attributes and state dynamics of memory devices are exploited to perform certain computational tasks in place with very high areal and energy efficiency. In a second level of brain-inspired computing using PCM devices, one could design a co-processor comprising multiple cross-bar arrays of PCM devices to accelerate training of deep neural networks. PCM technology could also play a key role in the space of specialized computing substrates for spiking neural networks and this can be viewed as the third level of brain-inspired computing using these devices.