  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.
