BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

We study deep neural networks and their use in semiparametric inference. We establish novel rates of convergence for deep feedforward neural nets. Our new rates are sufficiently fast (in some cases ...

A neural network is a machine learning model originally inspired by how the human brain works (Courtesy: Shutterstock/Jackie Niam) Precision measurements of theoretical parameters are a core element ...

Computational modelling, machine learning, and broader artificial (AI) intelligence approaches are now key approaches used to understanding and predicting ...

Gene regulatory networks (GRNs) depict the regulatory mechanisms of genes within cellular systems as a network, offering vital insights for understanding cell processes and molecular interactions that ...

A new technical paper titled “MultiVic: A Time-Predictable RISC-V Multi-Core Processor Optimized for Neural Network Inference” was published by researchers at FZI Research Center for Information ...

In 2026, neural networks are achieving unprecedented capabilities in workflow reasoning and cross-domain integration, yet benchmarks like MLRegTest expose persistent failures in rule abstraction and ...

Anker's THUS chips embeds a processors on memory chips to reduce the energy consumption. Apple has done something simililar by putting memory on a processing chip.