As scenes become ever more complex and real-time applications embrace ray tracing, path sampling algorithms that maximize quality at low sample counts become vital.

We aim to ask and answer an essential question “how quickly do we react after observing a displayed visual target?” To this end, we present psychophysical studies that characterize the remarkable disconnect between human saccadic behaviors and spatial visual acuity. Building on the results of our studies, we develop a perceptual model to predict temporal gaze behavior, particularly saccadic latency, as a function of the statistics of a displayed image.

Despite billions of hours of play and copious discussion online, mouse sensitivity recommendations for first-person targeting tasks vary by a factor of 10x or more and remain an active topic of debate in both competitive and recreational gaming communities.Inspired by previous academic literature in pointer-based gain optimization, we conduct the first user study of mouse sensitivity in first person targeting tasks, reporting a statistically significant range of optimal values in both task completion time and throughput. Due to inherent incompatibility (i.e., lack of convert-ability) b

Using technology in esports can address historical disparities such as gender, age, and ableism in professional play. To advance the field, it is important to focus on interdisciplinary esports research networks and communities. Topics relevant for future HCI/esports research include competitive physical sports versus esports, spectatorship, and inclusion.

Volumetric representations are popular in inverse rendering because they have a simple parameterization, are smoothly varying, and transparently handle topology changes. However, incorporating the full volumetric transport of light is costly and challenging, often leading practitioners to implement simplified models, such as purely emissive and absorbing volumes with "baked" lighting. One such challenge is the efficient estimation of the gradients of the volume's appearance with respect to its scattering and absorption parameters. We show that the straightforward approach—dif

Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations: a small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through stochastic gradient descent. The multiresolution structure allows the network to disambiguate hash collisions, making for

Pulse Amplitude Modulation (PAM) uses multiple voltage levels as different data symbols, transferring multiple bits of data simultaneously, thereby enabling higher communication bandwidth without increased operating frequencies. However, dividing the voltage into more symbols leads to a smaller voltage difference between adjacent symbols, making the interface more vulnerable to crosstalk and power noise. GDDR6X adopts four-level symbols (PAM4) with Maximum Transition Avoidance (MTA) coding, which reduces the effects of crosstalk.

In this talk, Yong He, a Senior Researcher at NVIDIA, shares recent advances and new features in the Slang shading language.

Lithography simulation is a critical step in VLSI design and optimization for manufacturability. Existing solutions for highly accurate lithography simulation with rigorous models are computationally expensive and slow, even when equipped with various approximation techniques. Recently, machine learning has provided alternative solutions for lithography simulation tasks such as coarse-grained edge placement error regression and complete contour prediction. However, the impact of these learning-based methods has been limited due to restrictive usage scenarios or low simulation accuracy.

(DAC 2022 preprint)

In this paper, we present GATSPI, a novel GPU accelerated logic gate simulator that enables ultra-fast power estimation for industry sized ASIC designs with millions of gates. GATSPI is written in PyTorch with custom CUDA kernels for ease of coding and maintainability. It achieves simulation kernel speedup of up to 1668X on a single-GPU system and up to 7412X on a multiple-GPU system when compared to a commercial gate-level simulator running on a single CPU core.