Engineers design structures that compute with heat

By leveraging excess heat instead of electricity, microscopic silicon structures could enable more energy-efficient thermal sensing and signal process

How to Manage CSS Performance for Websites

Every once in a while someone asks specifically about how to manage CSS performance for a website. What if there's a lot of components? What if there's a lot of animations? What if there's a lot of just CSS?

Federated Automatic Differentiation

Deep Learning with JAX

Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library.</b> The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. In Deep Learning with JAX</i> you will learn how to: Use JAX for numerical calculations</li> Build differentiable models with JAX primitives</li> Run distributed and parallelized computations with JAX</li> Use high-level neural network libraries such as Flax</li> Leverage libraries and modules from the JAX ecosystem</li> </ul> Deep Learning with JAX</i> is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment.

Reducing the energy consumption of software: Sebastian Erdweg receives ERC Consolidator Grant

Reducing the energy consumption of software: Sebastian Erdweg receives ERC Consolidator Grant

Swarming microrobots self-organize into diverse patterns

A research collaboration between Cornell and the Max Planck Institute for Intelligent Systems has found an efficient way to expand the collective behavior of swarming microrobots: Mixing different sizes of the micron-scale 'bots enables them to self-organize into diverse patterns that can be manipulated when a magnetic field is applied. The technique even allows the swarm to "cage" passive objects and then expel them.