Chalcogenide phase-change devices for neuromorphic photonic computing
Chalcogenide phase-change devices for neuromorphic photonic computing
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DOI：
10.1063/5.0042549
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发表时间：
2021-04-21
影响因子：
3.2
通讯作者：
Pernice, Wolfram H. P.
中科院分区：
文献类型：
--
作者：
Brueckerhoff-Plueckelmann, Frank;Feldmann, Johannes;Pernice, Wolfram H. P.
将人工智能系统集成到语音识别和自动驾驶等日常应用中，会迅速增加生成和处理的数据量。然而，由于冯·诺依曼瓶颈，用常规冯·诺依曼架构满足硬件要求仍然具有挑战性。因此，受人脑工作原理启发的新架构被开发出来，它们被称为神经形态计算。神经形态计算的关键原则是内存计算，以减少数据洗牌和并行化，以减少计算时间。相变光子学是神经形态计算的一个有前途的框架。通过切换到光域，并行化通过波分复用固有地成为可能，并且可以部署高调制速度。非易失性相变材料用于以能量有效的方式执行乘法和非线性运算。在这里，我们提出了两个原型的神经形态光子计算单元的硫系相变材料的基础上。首先是一个神经形态硬件加速器，旨在卷积神经网络中执行矩阵向量乘法。由于神经形态架构，该原型已经可以以每秒兆兆次累积的速度运行。第二种是全光脉冲神经元，它可以作为大规模人工神经网络的构建模块。在这里，整个计算在光域中进行，并且该设备仅需要用于数据输入和读出的电接口。
The integration of artificial intelligence systems into daily applications like speech recognition and autonomous driving rapidly increases the amount of data generated and processed. However, satisfying the hardware requirements with the conventional von Neumann architecture remains challenging due to the von Neumann bottleneck. Therefore, new architectures inspired by the working principles of the human brain are developed, and they are called neuromorphic computing. The key principles of neuromorphic computing are in-memory computing to reduce data shuffling and parallelization to decrease computation time. One promising framework for neuromorphic computing is phase-change photonics. By switching to the optical domain, parallelization is inherently possible by wavelength division multiplexing, and high modulation speeds can be deployed. Non-volatile phase-change materials are used to perform multiplications and non-linear operations in an energetically efficient manner. Here, we present two prototypes of neuromorphic photonic computation units based on chalcogenide phase-change materials. First is a neuromorphic hardware accelerator designed to carry out matrix vector multiplication in convolutional neural networks. Due to the neuromorphic architecture, this prototype can already operate at tera-multiply-accumulate per second speeds. Second is an all-optical spiking neuron, which can serve as a building block for large-scale artificial neural networks. Here, the whole computation is carried out in the optical domain, and the device only needs an electrical interface for data input and readout.