How AI Inference Sends Decision Making To The Edge

The next phase of AI infrastructure will not be defined by a single destination called “the cloud” or “the edge.” ...
The Next Platform

New Google Networks Tuned Up For GenAI Inference And Training

It is almost certainly not a coincidence that a networking expert at Google has risen to the top to be put in charge of the infrastructure development at the search engine, advertising, and now AI ...
EDN

The truth about AI inference costs: Why cost-per-token isn’t what it seems

The AI industry has converged on a deceptively simple metric: cost per token. It’s easy to understand, easy to compare, and easy to market. Every new system promises to drive it lower. Charts show ...
SDxCentral

Arrcus network fabric layer aims to direct AI inference traffic

Arrcus launched a new network fabric layer targeted at potential traffic bottlenecks caused by the growing use of AI inferencing services. The Arrcus Inference Network Fabric (AINF) is designed to ...
Network World

OpenAI turns to Cerebras in a mega deal to scale AI inference infrastructure

The multibillion-dollar deal shows how the growing importance of inference is changing the way AI data centers are designed and operated. OpenAI has signed a multibillion-dollar agreement to buy ...
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 ...
Semiconductor Engineering

Multi-Core Architecture Optimized For Time-Predictable Neural Network Inference (FZI, KIT)

A new technical paper titled “MultiVic: A Time-Predictable RISC-V Multi-Core Processor Optimized for Neural Network Inference” was published by researchers at FZI Research Center for Information ...
IEEE

High-Performance Bayesian Neural Network Inference Accelerator Based on FPGA

Abstract: Bayesian Neural Networks (BNNs) offer robust uncertainty estimation capabilities through probabilistic modeling, yet their prohibitively high computational complexity and resource ...