The field of computer hardware design is moving towards developing more robust and fault-tolerant systems. Researchers are exploring innovative approaches to achieve longevity in datapaths and accelerators, particularly in the face of increasing processor complexity. One notable direction is the integration of modular acceleration and fault tolerance, enabling systems to maintain performance even in the presence of faults. Another area of focus is the automation of circuit design, where techniques such as diffusion models and large language models are being leveraged to generate and optimize analog and digital circuits. These advancements have the potential to reduce data center costs, improve chip reliability, and expedite hardware development cycles. Noteworthy papers include: Oobleck, which proposes a novel architecture for fault-tolerant accelerators, and DiffCkt, which introduces a diffusion model-based framework for automatic transistor-level generation of analog circuits. Additionally, ChatHLS and CROP demonstrate the effectiveness of large language models in hardware design automation and optimization. AC-Refiner and Scalable Interconnect Learning also show promise in arithmetic circuit optimization and Boolean network scaling.