Nature

Energy-Efficient Deep Learning Architectures

Deep learning models have transformed fields from computer vision to natural language processing, yet their high computational and energy demands pose critical challenges for widespread deployment.
InfoQ

Maximizing Deep Learning Performance on CPUs using Modern Architectures

Bibek Bhattarai details Intel's AMX, highlighting its role in accelerating deep learning on CPUs. He explains how AMX optimizes GEMM operations using low-precision data (INT8, BF16) and tile-based ...
eWeek

Types of AI Models: A Deep Dive into AI Architecture

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...
VentureBeat

Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...