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?”
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Neural networks news
Intel NN News
- KVCrush: Rethinking KV Cache Alternative Representation for Faster LLM Inference
Developed by Intel, KVCrush can improve LLM inference throughput up to 4x with less than 1% […]
- Scaling AI with Confidence: Lenovo’s Approach to Responsible and Practical Adoption
In the race to operationalize AI, success depends not on flashy pilots, but on turning […]
- Unlocking AI-Driven Media Monetization with Intel® Xeon® CPUs and Broadpeak BannersIn2
In this article, we will cover how to deploy high-performance AI inferencing for media data […]
- KVCrush: Rethinking KV Cache Alternative Representation for Faster LLM Inference
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