This article presents an inverter-based short-reach ac-coupled toggle (ISR-ACT) link targeted for short-reach die-to-die communication over silicon interposer or similar high-density interconnect. The ISR-ACT’s transmitter (TX) sends non-return-to-zero (NRZ) data through a small series capacitor to inject low-swing pulses into the line. These pulses are amplified and latched by a two-stage receiver (RX), where the 1st-stage transimpedance amplifier (TIA) amplifies the pulse data and positive feedback around both stages captures the data and maintains the dc level on the line.

We present XCube, a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model can generate millions of voxels with a finest effective resolution of up to $1024^3$ in a feed-forward fashion without time-consuming test-time optimization. To achieve this, we employ a hierarchical voxel latent diffusion model which generates progressively higher resolution grids in a coarse-to-fine manner using a custom framework built on the highly efficient VDB data structure.

Neural networks efficiently encode learned information within their parameters. Consequently, many tasks can be unified by treating neural networks themselves as input data. When doing so, recent studies demonstrated the importance of accounting for the symmetries and geometry of parameter spaces. However, those works developed architectures tailored to specific networks such as MLPs and CNNs without normalization layers, and generalizing such architectures to other types of networks can be challenging.

Text-to-3D modeling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields. DreamFusion recently achieved high-quality results but requires a lengthy, per-prompt optimization to create 3D objects. To address this, we amortize optimization over text prompts by training on many prompts simultaneously with a unified model, instead of separately. With this, we share computation across a prompt set, training in less time than per-prompt optimization.

Studies have shown that action video game players have enhanced visual abilities in various domains, such as multiple object tracking, size of the useful field of view, and visual search speed and accuracy. These improvements have been attributed to either a general advantage in “learning to learn” abilities, or domain-specific enhancement(s) in the “common demands” between specific games and experimental tasks. To investigate these two theories, we conducted six experiments examining whether and how players and non-players differ in various aspects of visual search.

Computer graphics research has long prioritized image quality over frame rate. Yet demand for an alternative is growing, with many esports players turning off visual effects to improve frame rates. Is it time for graphics researchers to reconsider their goals? A workshop at the 2023 SIGGRAPH Conference explored this question. Three researchers made provocative presentations, each of which were then discussed by dozens of research and industry attendees.