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With the ever-increasing hardware design complexity comes the realization that efforts required for hardware verification increase at an even faster rate. Driven by the push from the desired verification productivity boost and the pull from leap-ahead capabilities of machine learning (ML), recent years have witnessed the emergence of exploiting ML-based techniques to improve the efficiency of hardware verification. In this article, we present a panoramic view of how ML-based techniques are embraced in hardware design verification, from formal verification to simulation-based verification, from academia to industry, and from current progress to future prospects. We envision that the adoption of ML-based techniques will pave the road for more scalable, more intelligent, and more productive hardware verification. 

This work reports the thermal properties of garnet electrolyte LLZTO. The aged LLZTO exhibits an enhanced thermal conductivity, attributed to the formation of Li₂CO₃. 

Striking a balance between minimizing bandwidth consumption and maintaining high visual quality stands as the paramount objective in volumetric content delivery. However, achieving this ambitious target is a substantial challenge, especially for mobile devices with constrained computational resources, given the voluminous amount of 3D data to be streamed, strict latency requirements, and high computational load. Inspired by the advantages offered by neural radiance fields (NeRF), we propose, for the first time, to deliver volumetric videos by utilizing neural-based content representations. We delve deep into potential challenges and explore viable solutions for both video-on-demand (VOD) and live video streaming services, in terms of the end-to-end pipeline, real-time and high-quality streaming, rate adaptation, and viewport adaptation. Our preliminary results lend credence to the feasibility of our research proposition, offering a promising starting point for further investigation.