The exponential growth of data in genomics and artificial intelligence (AI) has highlighted the limitations of traditional Von Neumann architectures, particularly their inefficiency in handling data-intensive applications. These systems suffer from high energy consumption and latency due to excessive data movement between memory and processing units. To address these challenges, this research proposes memory-centric hardware accelerator designs using 3D NAND Flash and 3D DRAM technologies. The goal is to develop novel architectures that leverage in-memory and near-memory computing to minimize data movement and improve energy efficiency and scalability.The proposed designs include a 3D NAND Flash-based hyperdimensional computing (HDC) engine for genome sequencing and mass spectrometry, and a 3D DRAM-centric accelerator for large language models (LLM). By performing computations directly within or near memory arrays, these architectures aim to overcome the computational bottlenecks of traditional systems. Simulation results will benchmark the proposed designs against existing GPU-based accelerators, focusing on key metrics such as energy consumption, latency, memory density, and computational throughput. This research contributes to the development of efficient and scalable memory architectures for data-intensive applications in genomics and AI.