Resistive Random-Access Memory (ReRAM)-based Processing In-Memory (PIM) Accelerator has emerged as a promising computing architecture for memory-intensive applications, such as Deep Neural Networks (DNNs). However, due to its immaturity, ReRAM devices often suffer from various reliability issues, which hinder the practicality of the PIM architecture and lead to a severe degradation in DNN accuracy. Among various reliability issues, device variation and offset current from High Resistance State (HRS) cell have been considered as major problems in a ReRAM-based PIM architecture. Due to these problems, the throughput of the ReRAM-based PIM is reduced as fewer wordlines are activated. In this paper, we propose VECOM, a novel approach that includes a variation-resilient encoding technique and an offset compensation scheme for a robust ReRAM-based PIM architecture. The first technique (i.e., VECOM encoding) is built based on the analysis of the weight pattern distribution of DNN models, along with the insight into the ReRAM’s variation property. The second technique, VECOM offset compensation, tolerates offset current in PIM by mapping the conductance of each Multi-level Cell (MLC) level added with a specific offset conductance. Experimental results in various DNN models and datasets show that the proposed techniques can increase the throughput of the PIM architecture by up to 9.1 times while saving 50% of energy consumption without any software overhead. Additionally, VECOM is also found to endure low R-ratio ReRAM cell (up to 7) with a negligible accuracy drop.