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The component that accelerates matrix multiplications in GPU applications is Tensor Cores. Tensor Cores are specialized processing units designed specifically for deep learning applications and high-performance computing tasks that involve matrix calculations. They optimize the execution of tensor operations by allowing for faster computations, especially in deep learning frameworks where matrix multiplications are prevalent.

Tensor Cores enhance performance by enabling the execution of mixed-precision calculations, which allow for faster calculations without significantly sacrificing accuracy. This is particularly beneficial in training neural networks where large amounts of data and numerous parameters are involved, making efficient matrix operations critical.

While CUDA Cores are also important for general GPU computations, they do not offer the specialized acceleration for matrix multiplications that Tensor Cores provide. CPU Threads are designed for tasks executed on the CPU, and while they can perform computations, they are not specifically focused on matrix multiplications as GPUs are. Memory Chips support data storage and retrieval but do not directly engage in calculations. Thus, the unique architecture and capabilities of Tensor Cores make them the correct answer in this context.