With the rapid evolution of fifth-generation (5G) and the emergence of sixth-generation (6G) wireless communication systems, the need for large-scale, real-time data processing has become increasingly critical across a wide range of applications. This growth is paralleled by the exponential rise in AI model complexity and data generation, driving demand for hardware architectures that can efficiently handle high-throughput, low-latency computation. Despite these advances, hardware development for data-intensive systems has significantly lagged behind, creating a bottleneck for practical deployment. This dissertation addresses this gap by proposing novel hardware solutions in three key areas: (1) a system-level RF radar emulator based on the Direct Path Computing Model; (2) the first digital ultra-low-bit precision linear embedded beamforming system with quantization compensation; and (3) a Processing-in-Memory (PIM) computing architecture optimized for AI-enhanced automotive radar applications. Each contribution is designed to overcome existing limitations in data movement, scalability, and power efficiency, offering a cohesive framework for next-generation RF and AI communication systems. Collectively, this work advances the development of high-performance, energy-efficient, and scalable hardware platforms capable of meeting the growing computational demands of the 6G era and beyond.