The field of hardware design is experiencing a significant shift with the integration of large language models (LLMs). Recent developments have shown that LLMs can be effectively used for power, performance, and area (PPA) estimation, RTL generation, and hardware implementation. The use of LLMs has the potential to streamline hardware development, improve accessibility, and foster a collaborative workflow between hardware and algorithm engineers. Noteworthy papers include RocketPPA, which introduces a novel framework for PPA estimation using a chain-of-thought technique and a mixture-of-experts architecture, resulting in significant improvements in estimation accuracy. ReaLM is another notable paper, which proposes a statistical algorithm-based fault tolerance technique for reliable and efficient LLM inference, reducing perplexity degradation and improving energy efficiency. TuRTLe, a unified evaluation framework for LLMs in RTL generation, provides a comprehensive assessment of LLM performance in syntax correctness, functional correctness, synthesis, PPA optimization, and exact line completion.