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.

A powerful Go framework for building production-ready AI agents that seamlessly integrates memory management, tool execution, multi-LLM support, and enterprise features into a flexible, extensible ...

Memory chips are a key component of artificial intelligence data centers. The boom in AI data center construction has caused a shortage of semiconductors, which are also crucial for electronics like ...

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, ...