Compute in memory(CIM) is a promising approach for efficiently performing data-centric computing (such as neural network computations). Among the multiple semiconductor memory technologies, embedded DRAM (eDRAM), which integrates the DRAM bitcell with high performance logic transistors can enable efficient CIM designs. However, Silicon based eDRAM technology suffers from poor retention time incurring significant refresh power overhead. However, eDRAM using back end of the line integrated C-axis aligned crystalline (CAAC) Indium Gallium Zinc Oxide transistors (IGZO), exhibiting extreme low leakage is promising memory technology with lower refresh power overhead. Long retention time in IGZO eDRAM can enable multi-level cell functionality which can improve its efficacy in CIM applications. In this article, we explore capacitorless IGZO eDRAM based multi-level cell, capable of storing 1.5bits/cell for CIM designs focussed on DNN inference applications. We perform a detailed design space exploration of IGZO eDRAM sensitivity to process, temperature variations for read, write and retention operations followed by architecture level simulations comparing performance and energy for different workloads. The effectiveness of IGZO eDRAM based CIM architecture is evaluated using a representative neural network and the proposed approach achieves 82% Top-1 inference accuracy for the CIFAR-10 dataset, compared to 87% software accuracy with high bitcell storage density.