Neural network scaling has been critical for improving the model quality in many real-world
machine learning applications with vast amounts of training data and compute. Although this …

Abstract We present Reversible Vision Transformers, a memory efficient architecture design
for visual recognition. By decoupling the GPU memory footprint from the depth of the model …

We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum
physics. NetKet is built around neural-network quantum states and provides efficient …

This review presents an overview of the current state‐of‐the‐art in photonics computing,
which leverages photons, photons coupled with matter, and optics‐related technologies for …

The development of microelectronics is always driven by reducing transistor size and
increasing integration, from the initial micron-scale to the current few nanometers. The …

Cloud providers typically use air-based solutions for cooling servers in datacenters.
However, increasing transistor counts and the end of Dennard scaling will result in chips …

Deep Learning (DL) models have achieved superior performance. Meanwhile, computing
hardware like NVIDIA GPUs also demonstrated strong computing scaling trends with 2x …

Plastic has been detected in the ocean in most locations where scientists have looked for it.
While ubiquitous in the environment, plastic pollution is heterogeneous, and plastics of …

Massive-MIMO systems can achieve high capacities and data rates by increasing the
number of operational antennas in the base station. As more antenna elements are …

Monitoring laser ablation when using high power lasers can be challenging due to plasma
obscuring the view of the machined sample. Whilst the appearance of the generated plasma …