NextBigFuture

Analog in-memory Computing Attention Mechanism for Fast and Energy-efficient Large Language Models

A Nature paper describes an innovative analog in-memory computing (IMC) architecture tailored for the attention mechanism in large language models (LLMs). They want to drastically reduce latency and ...
Communications of the ACM

Can We Understand How Large Language Models Reason?

Large language models can write essays, solve math problems, and generate computer code, but it’s not fully understood how ...
Hosted on MSN

Google’s TurboQuant claims 6x lower memory use for large AI models

Google researchers have proposed TurboQuant, a method for compressing the key-value caches that large language models rely on during inference. In a preprint, the team reports up to six times lower KV ...
Semiconductor Engineering

HW-based Heterogeneous Memory Management for LLM Inferencing (KAIST, Stanford Unversity)

A new technical paper titled “Hardware-based Heterogeneous Memory Management for Large Language Model Inference” was published by researchers at KAIST and Stanford University. “A large language model ...
Wired

Do Large Language Models Dream of AI Agents?

During sleep, the human brain sorts through different memories, consolidating important ones while discarding those that don’t matter. What if AI could do the same? Bilt, a company that offers local ...
ascopubs.org

Enhancing Oncology-Specific Question Answering With Large Language Models Through Fine-Tuned Embeddings With Synthetic Data

Enhancing Readability of Lay Abstracts and Summaries for Urologic Oncology Literature Using Generative Artificial Intelligence: BRIDGE-AI 6 Randomized Controlled Trial The recent advancements of ...
Semiconductor Engineering

Small Vs. Large Language Models

The proliferation of edge AI will require fundamental changes in language models and chip architectures to make inferencing and learning outside of AI data centers a viable option. The initial goal ...