Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, China.
Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
Nat Nanotechnol. 2020 Sep;15(9):776-782. doi: 10.1038/s41565-020-0722-5. Epub 2020 Jun 29.
In the nervous system, dendrites, branches of neurons that transmit signals between synapses and soma, play a critical role in processing functions, such as nonlinear integration of postsynaptic signals. The lack of these critical functions in artificial neural networks compromises their performance, for example in terms of flexibility, energy efficiency and the ability to handle complex tasks. Here, by developing artificial dendrites, we experimentally demonstrate a complete neural network fully integrated with synapses, dendrites and soma, implemented using scalable memristor devices. We perform a digit recognition task and simulate a multilayer network using experimentally derived device characteristics. The power consumption is more than three orders of magnitude lower than that of a central processing unit and 70 times lower than that of a typical application-specific integrated circuit chip. This network, equipped with functional dendrites, shows the potential of substantial overall performance improvement, for example by extracting critical information from a noisy background with significantly reduced power consumption and enhanced accuracy.
在神经系统中，树突是神经元的分支，负责在突触和神经元体之间传递信号，在处理功能方面发挥着关键作用，例如对突触后信号进行非线性整合。人工神经网络缺乏这些关键功能，会影响其性能，例如在灵活性、能效和处理复杂任务的能力方面。在这里，通过开发人工树突，我们使用可扩展的忆阻器器件，实验证明了一种完全集成有突触、树突和神经元体的完整神经网络。我们执行了数字识别任务，并使用实验得出的器件特性模拟了一个多层网络。与中央处理器相比，其功耗降低了三个数量级以上，与典型的专用集成电路芯片相比，功耗降低了 70 倍。这个配备了功能树突的网络，显示出了大幅提高整体性能的潜力，例如通过以显著降低的功耗和更高的准确性从嘈杂的背景中提取关键信息。
