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.
AI agents are a risky business. Even when stuck inside the chatbox window, LLMs will make mistakes and behave badly. Once ...
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 ...
Abstract: Memory safety violations in low-level code, written in languages like C, continues to remain one of the major sources of software vulnerabilities. One method of removing such violations by ...
Online LLM inference powers many exciting applications such as intelligent chatbots and autonomous agents. Modern LLM inference engines widely rely on request batching to improve inference throughput, ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results