Near Memory Compute is becoming important for future AI processing systems that need improvement in system performance and energy-efficiency. The Von Neumann computing model requires data to commute from memory to compute and this data movement burns energy. Is it time for NMC to solve this data movement bottleneck? This blog addresses this question and is inspired by Intel Fellow, Dr. Frank Hady’s recent presentation at the International Solid State Circuits Conference (ISSCC), titled “We have rethought our commute; Can we rethink our data’s commute?”
-
-
Articles récents
- Next-Gen AI Inference: Intel® Xeon® Processors Power Vision, NLP, and Recommender Workloads
- Document Summarization: Transforming Enterprise Content with Intel® AI for Enterprise RAG
- AutoRound Meets SGLang: Enabling Quantized Model Inference with AutoRound
- In-production AI Optimization Guide for Xeon: Search and Recommendation Use Case
- Argonne’s Aurora Supercomputer Helps Power Breakthrough Simulations of Quantum Materials
-
Neural networks news
Intel NN News
- Next-Gen AI Inference: Intel® Xeon® Processors Power Vision, NLP, and Recommender Workloads
Intel® Xeon® processors can deliver a CPU-first platform built for modern AI workloads without […]
- Document Summarization: Transforming Enterprise Content with Intel® AI for Enterprise RAG
Transform enterprise documents into insights with Document Summarization, optimized for Intel® […]
- AutoRound Meets SGLang: Enabling Quantized Model Inference with AutoRound
We are thrilled to announce an official collaboration between SGLang and AutoRound, enabling […]
- Next-Gen AI Inference: Intel® Xeon® Processors Power Vision, NLP, and Recommender Workloads
-