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New algorithm CAMP optimizes LLM mapping on 3D-IMC arrays

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CAMP, a novel algorithm designed to optimize the placement of Large Language Model (LLM) weights on 3D-In-Memory Computing (3D-IMC) arrays. This method addresses the challenge of maximizing concurrent computation by analyzing potential resource contention based on architecture, network, and runtime context. By employing a profiling-driven feedback loop, CAMP improves 3D-IMC compute utilization from 42.4% to 60.3%, leading to a significant increase in overall throughput. AI

IMPACT Optimizes LLM inference efficiency, potentially reducing latency and energy consumption for AI applications.

RANK_REASON The cluster describes a new algorithm presented in a research paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm CAMP optimizes LLM mapping on 3D-IMC arrays

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The cluster describes a new algorithm presented in a research paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Orhun G\"orkem, Athanasios Vasilopoulos, Georgios Pagonis, Victoria Clerico, Hadjer Benmeziane, Irem Boybat, Hsinyu Tsai, Abu Sebastian, William Andrew Simon ·

    CAMP: Context-Aware Mapping and Placement for LLMs on 3D-IMC Arrays

    arXiv:2611.07707v1 Announce Type: cross Abstract: Large Language Models (LLMs) incur severe memory-access overheads due to the cost of loading large weight matrices and key-value (KV) caches. Analog In-Memory Computing (AIMC) is an emerging solution that enables computation direc…

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