Google unveils TurboQuant to reduce AI model memory usage

Google introduces TurboQuant, a compression method that reduces memory usage and increases speed ...

The Five Trends Driving Memory To The Forefront Of AI Scaling

Memory is no longer just supporting infrastructure; it's now become a primary determinant of system performance, cost and ...
TweakTown

SK hynix and Samsung team up to standardize LPDDR6-Processing In Memory (PIM) for on-device AI

TL;DR: South Korean memory rivals SK hynix and Samsung team up to expedite LPDDR6-PIM (Processing-In-Memory) technology for the future of on-device AI. SK hynix and Samsung are massive memory rivals ...
Semiconductor Engineering

Developing ReRAM As Next Generation On-Chip Memory For Machine Learning, Image Processing And Other Advanced CPU Applications

In modern CPU device operation, 80% to 90% of energy consumption and timing delays are caused by the movement of data between the CPU and off-chip memory. To alleviate this performance concern, ...
EDN

AI at the edge: It’s just getting started

Artificial intelligence (AI) is expanding rapidly to the edge. This generalization conceals many more specific advances—many kinds of applications, with different processing and memory requirements, ...
SDxCentral

AI inference crisis: Google engineers on why network latency and memory trump compute

Google researchers have warned that large language model (LLM) inference is hitting a wall amid fundamental problems with memory and networking problems, not compute. In a paper authored by ...