Universal Approach for Calibrating Large-Scale Electronic and Photonic Crossbar Arrays

Analog electronic and photonic crossbar arrays have been emerging as energy-efficient hardware implementations to accelerate computationally intensive general matrix-vector and matrix-matrix multiplications in machine learning (ML) algorithms. However, the inevitable nonuniformity in large-scale electronic and optoelectronic devices and systems prevents scalable deployment. Herein, a calibration approach is reported that enables accurate calculations in crossbar arrays despite hardware imperfections. This approach is experimentally validated in a small-scale free-space photonic crossbar array based on cascaded spatial light modulators and demonstrated the scalability and universality of this approach in various large-scale electronic and photonic crossbar arrays. The improved performance of calibrated crossbar arrays in an ML model inference is further demonstrated to classify handwritten digital images.