论文标题
基于石墨烯光电设备的人工智能加速器
Artificial Intelligence Accelerators based on Graphene Optoelectronic Devices
论文作者
论文摘要
执行矩阵矢量乘法（MVM）操作的光电和光电方法表明，具有前所未有的性能加速机器学习（ML）算法的巨大希望。纳米材料将纳米材料纳入系统可以进一步提高性能，但由于其非凡的特性，宏观量表中纳米结构的不均匀性和变化构成了大规模硬件部署的严重限制。在这里，我们报告了一种新的光电体系结构，该体系结构包括由石墨烯制成的空间照明调制器和光电探测器阵列来执行MVM。石墨烯，近零功率消耗的电声控制和极端平行性的超高载体迁移率提示超高数据吞吐量和超级功率消耗。此外，我们开发了一种用不完美的组件执行准确计算的方法，为可扩展系统奠定了基础。最后，我们执行一些代表性的ML算法，包括奇异值分解，支持向量机和深神经网络，以显示我们平台的多功能性和通用性。
Optical and optoelectronic approaches of performing matrix-vector multiplication (MVM) operations have shown the great promise of accelerating machine learning (ML) algorithms with unprecedented performance. The incorporation of nanomaterials into the system can further improve the performance thanks to their extraordinary properties, but the non-uniformity and variation of nanostructures in the macroscopic scale pose severe limitations for large-scale hardware deployment. Here, we report a new optoelectronic architecture consisting of spatial light modulators and photodetector arrays made from graphene to perform MVM. The ultrahigh carrier mobility of graphene, nearly-zero-power-consumption electro-optic control, and extreme parallelism suggest ultrahigh data throughput and ultralow-power consumption. Moreover, we develop a methodology of performing accurate calculations with imperfect components, laying the foundation for scalable systems. Finally, we perform a few representative ML algorithms, including singular value decomposition, support vector machine, and deep neural networks, to show the versatility and generality of our platform.
