In-memory computing using memristive devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. Synaptic units comprising one or more memristive devices organized in a crossbar array configuration are capable of performing the matrix-vector multiply operations in place, by exploiting the Kirchhoff’s circuits laws. In this Thesis, a weight mapping algorithm is designed, in order to efficiently program such a synaptic unit, comprising four phase-change memory (PCM) devices, to target conductance values. To evaluate the programming scheme, a simulator based on the measured programming characteristics of 10,000 PCM devices is developed in a MATLAB environment. It is shown that the synaptic unit can be programmed reliably without significant overhead in programming time or energy compared to a unit comprising a single PCM device, while gaining resilience to device-level non-idealities and yield.