In particular, generative AI workloads can exacerbate network complexity in four ways.

A novel framework is proposed that combines multiresonance biosensors with machine learning (ML) to significantly enhance the accuracy of parameter prediction in biosensing. Unlike traditional ...

In-cache computing technology transforms existing caches into long-vector compute units and offers low-cost alternatives to building expensive vector engines for mobile CPUs. Unfortunately, existing ...

At QCon London 2025, Teena Idnani from Microsoft addressed the rise of multi-cloud adoption, revealing that 89% of organizations embrace this strategy. Using the fictional FinBank, she showcased ...

Zhang et al. (1) question whether our study (2) provides evidence of multiple parallel vector memories coexisting in bumblebees. They suggest that an alternate model, where a single vector memory is ...

Parallels between our brains and vector databases go deeper than retrieval. Both excel at compression, organizing and identifying patterns.

FAIR at Meta and Stanford University researchers introduced a new architecture called Mixture-of-Transformers (MoT). The MoT, built as a sparse, multi-modal transformer, reduces computational demands ...

A new technical paper titled “NeuroVM: Dynamic Neuromorphic Hardware Virtualization” was published by researchers at Stanford University, UT Austin and Temsa Research & Development Center. Abstract ...

We look at the use of vector data in AI, how vector databases work, plus vector embedding, the challenges for storage of vector data and the key suppliers of vector database products ...