The growth of machine learning, combined with the approaching limits of conventional digital computing, are driving a search for alternative and complementary forms of …
Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning,,–, owing to their reputed difficulty among the world's best …
In response to innovations in machine learning (ML) models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its …
Google deployed several TPU generations since 2015, teaching us lessons that changed our views: semi-conductor technology advances unequally; compiler compatibility trumps …
Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and …
Today's floating-point arithmetic landscape is broader than ever. While scientific computing has traditionally used single precision and double precision floating-point arithmetics, half …
Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with …
Large deep learning models have shown great potential with state-of-the-art results in many tasks. However, running these large models is quite challenging on an accelerator (GPU or …
The quest for determinism in machine learning has disproportionately focused on characterizing the impact of noise introduced by algorithmic design choices. In this work, we …