When we talk about the cost of AI infrastructure, the focus is usually on Nvidia and GPUs — but memory is an increasingly important part of the picture. As hyperscalers prepare to build out billions ...

Abstract: For the first time, a ferroelectric (FE)-based key-value (KV) cache for large language models (LLMs) is proposed and experimentally demonstrated. Through device-architecture-algorithm ...

Low-rank data analysis has emerged as a powerful paradigm across applied mathematics, statistics, and data science. With the rapid growth of modern datasets in size, dimensionality, and complexity, ...

The Memory Temperature Principle: Source code and paper for the novel observation on CPU cache behavior and the Pre-Warming Ceremony technique.

According to Jeff Dean on Twitter, concrete examples of various AI performance optimization techniques have been provided, including high-level descriptions from a 2001 set of changes. These examples ...

Long sales cycles, low conversion volume, and multi-stage purchase journeys make measurement and attribution harder, creating real obstacles to campaign optimization. For B2Bs and brands selling ...

Abstract: This paper proposes a productivity optimization model based on mathematical modeling, combined with an improved optimization algorithm, aiming to improve the computational efficiency and ...

High-performance limit order book engine with C++ core and Python SDK. Processes 20M+ msgs/sec with µs latency. Supports real crypto/equity data replay, spread/imbalance/impact analytics, and ...

Reinforcement learning (RL) plays a crucial role in scaling language models, enabling them to solve complex tasks such as competition-level mathematics and programming through deeper reasoning.