In this paper, we discuss the source of effectiveness of Graph Neural Networks (GNNs) in EDA, particularly in the VLSI design automation domain. We argue that the effectiveness comes from the fact that GNNs implicitly embed the prior knowledge and inductive biases associated with given VLSI tasks, which is one of the three approaches to make a learning algorithm physics-informed. These inductive biases are different to those common used in GNNs designed for other structured data, such as social networks and citation networks.

GPU-based accelerated computing is powering the AI revolution. These systems include processors and switches which push thermal power density limits while demanding large I/O bandwidths. To continue scaling, very dense integration of ultra-efficient optical transceivers is called for to alleviate current inefficiencies in off-package signalling.

Transformers have attained superior performance in natural language processing and computer vision. Their self-attention and feedforward layers are overparameterized, limiting inference speed and energy efficiency. Tensor decomposition is a promising technique to reduce parameter redundancy by leveraging tensor algebraic properties to express the parameters in a factorized form. Prior efforts used manual or heuristic factorization settings without hardware-aware customization, resulting in poor hardware efficiencies and large performance degradation. 

Which display size helps gamers win? Recommendations from the research and PC gaming communities are contradictory. We find that as display size grows, targeting performance improves. When size increases from 13" to 26", targeting time drops by over 3%. Further size increases from 26" through 39", 52" and 65", bring more modest improvements, with targeting time dropping a further 1%. While such improvements may not be meaningful for novice gamers, they are extremely important to skilled and competitive players.

Historically, much research and development in human computer interaction has focused on atomic and generalizable tasks, where task completion time indicates productivity. However, the emergence of competitive games and esports reminds us of an alternative perspective on human performance in HCI: mastery of higher-level, holistic practices. Just as a world-renowned artist is rarely evaluated for their individual brush strokes, so skilled competitive gamers rarely succeed solely by completing individual mouse movements or keystrokes as quickly as possible.