GEMM (General Matrix Multiplication) is a fundamental operation in deep learning (DL), serving as the key computing kernel for neural network layers, such as convolutional and fully connected layers. As the deployment of DL models expands beyond high-performance computing to resource-constrained embedded systems, there is a growing demand for efficient GEMM implementations that can meet strict power budgets and real-time processing requirements. This paper introduces a dual-issue in-order RISC-V core with an inpipeline GEMM accelerator based on the VeeR EH1 core [13], eliminating control signals and data transfer overhead inherent to coprocessors and standalone accelerators. By integrating GEMM execution directly into the pipeline, the design leverages core utilization and enables compiler-time preloading of operands, streamlining memory access and reducing dynamic branching, thereby minimizing branch mispredictions. Further, pipelined load/store operations mitigate memory access stalls and cache misses, thereby enhancing the pipeline throughput. Speedup, measured using Verilator simulation, demonstrates an average of ∼ 5 × over the reference VeeR EH1 baseline core. By integrating GEMM acceleration within the pipeline, this work bridges the gap between domain-specific accelerators and general-purpose cores, delivering scalable computing performance.