Transformations are the key to such codes, and they rely on math that predates computing as we know it by centuries. There ...

The third quarter of 2025 was dominated by massive rounds for companies developing AI chips and quantum computers. Over $2.5 billion went to AI, with wafer-scale chip maker Cerebras leading the pack ...

This study introduces an optical neural network (ONN)-based autoencoder for efficient image processing, utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks. To ...

Most linear algebra courses start by considering how to solve a system of linear equations. \[ \begin{align} a_{0,0}x_0 + a_{0,1}x_0 + \cdots a_{0,n-1}x_0 & = b_0 ...

We continue our journey into Artificial Neural Networks (ANNs) before moving on to Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Transformers in the following series ...

PyXHDL born for developers who are not really in love with any of the HDL languages and instead appreciate the simplicity and flexibility of using Python for their workflows. PyXHDL allows to write ...

Matrix-vector multiplications form the core of a plethora of scientific computing and machine learning applications that include solving partial differential equations, forward and back propagation in ...

Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, ...

This paper concerns the more foundational tasks of distributed dense linear algebra. While a single TPU core can already store and operate on large matrices (e.g., of size 16,384, 32,768 in single ...

The calculation of derivatives is ubiquitous in science and engineering. In thermodynamics, in particular, state properties can be expressed as derivatives of thermodynamic potentials. The manual ...