IBM Research- Europe, Säumerstrasse, 8803, Rüschlikon, Switzerland.
Department of Materials, University of Oxford, Oxford, OX1 3PH, Oxford, UK.
Nat Commun. 2022 Apr 26;13(1):2247. doi: 10.1038/s41467-022-29870-9.
Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase the efficiency of basic neural operations. However, powerful mechanisms such as reinforcement learning and dendritic computation require more advanced device operations involving multiple interacting signals. Here we show that nano-scaled films of chalcogenide semiconductors can perform such multi-factor in-memory computation where their tunable electronic and optical properties are jointly exploited. We demonstrate that ultrathin photoactive cavities of Ge-doped Selenide can emulate synapses with three-factor neo-Hebbian plasticity and dendrites with shunting inhibition. We apply these properties to solve a maze game through on-device reinforcement learning, as well as to provide a single-neuron solution to linearly inseparable XOR implementation.
神经形态硬件模拟生物计算是人工智能发展的关键驱动力。例如，忆阻技术，包括基于硫属化物的内存计算概念，已被用于显著加速和提高基本神经操作的效率。然而，诸如强化学习和树突计算等强大机制需要更先进的设备操作，涉及多个相互作用的信号。在这里，我们展示了纳米级的硫属半导体薄膜可以执行这种多因素的内存计算，其中联合利用了其可调谐的电子和光学性质。我们证明了掺锗硒化物的超薄光活性腔可以模拟具有三因素新赫比可塑性的突触和具有分流抑制的树突。我们将这些特性应用于通过设备内强化学习来解决迷宫游戏，以及提供单个神经元解决方案来实现线性不可分的异或实现。
