The objective of the proposed research is to address the critical challenges posed by the increasing demand for efficient deep learning solutions. Tasks such as object detection in images rely on additional sensors, while natural language processing with large language models (LLMs) necessitates high memory capacities and efficient inter-component communication. Heterogeneous integration has recently emerged as a promising solution to improve data and signal movement across components. This dissertation presents designs for a range of applications, from sensing-based edge technologies to hardware accelerators for server-based LLMs. The proposed work explores and evaluates various packaging schemes tailored to application-specific requirements, emphasizing the integration of emerging memory technologies for enhanced performance. Furthermore, it addresses the thermal management challenges inherent to heterogeneous integration. Comprehensive evaluations are conducted, benchmarking proposed designs against the state-of-the-art and offering a methodology for electrical-thermal system-level co-design. This work provides critical insights into future memory and interconnect design, identifying bottlenecks and guiding efficient hardware development for advanced deep learning applications.