Stochastic Computing (SC) is an alternative computing paradigm that promises high robustness to noise and outstanding area- and power-efficiency compared to the traditional binary. It also enables the design of fully parallel and scalable computations. Despite its advantage, SC suffers from long latency and high energy consumption, especially with current CMOS technology. The cost of conversion between binary and stochastic representation takes a significant cost with CMOS circuits. In-Memory Computing (IMC) is introduced to accelerate Big Data applications by removing the data movement between memory and processing units, and by providing massive parallelism. In this work, we explore the efforts in employing IMC for fast and energy-efficient SC system design. We specially focus on memristors as an emerging technology that promises efficient memory and computation beyond CMOS. We discuss the potentials and challenges for realizing efficient SC systems in memory.