Abstract: Processing-In-Memory (PIM) architectures alleviate the memory bottleneck in the decode phase of large language model (LLM) inference by performing operations like GEMV and Softmax in memory.

The saying “round pegs do not fit square holes” persists because it captures a deep engineering reality: inefficiency most often arises not from flawed components, but from misalignment between a ...

OntoMem is built on the concept of Ontology Memory—structured, coherent knowledge representation for AI systems. Give your AI agent a "coherent" memory, not just "fragmented" retrieval. Traditional ...

Abstract: On-device Large Language Model (LLM) inference enables private, personalized AI but faces memory constraints. Despite memory optimization efforts, scaling laws continue to increase model ...

At the start of 2025, I predicted the commoditization of large language models. As token prices collapsed and enterprises moved from experimentation to production, that prediction quickly became ...