CIMERA: Compute-in-Interconnect and Memory with Reconfigurable Precision for LLM Inference

Yue Jiet Chong; Yimin Wang; Wei Zhang; Xuanyao Fong

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arXiv · 2607.13649

CIMERA: Compute-in-Interconnect and Memory with Reconfigurable Precision for LLM Inference

Yue Jiet Chong·Yimin Wang·Wei Zhang·Xuanyao Fong

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Abstract

LLM impose significant computational and memory demands, creating challenges for energy-efficient inference across platforms ranging from data centers to power-constrained edge devices. Weight precision plays a critical role in balancing inference accuracy, throughput, and energy consumption, while modern LLM workloads exhibit pronounced heterogeneity and tolerance that favors adaptive precision execution. This paper presents CIMERA, a reconfigurable-precision LLM inference accelerator that integrates compute-in-interconnect and memory to mitigate the memory wall and enable precision-aware execution. Compared to Nvidia H100, CIMERA delivers up to $25\times$ and $10\times$ higher energy efficiency for 1B and 13B models, respectively.

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BibTeX RIS

Yue Jiet Chong, Yimin Wang, Wei Zhang, Xuanyao Fong. 2026-07-15. CIMERA: Compute-in-Interconnect and Memory with Reconfigurable Precision for LLM Inference. https://arxiv.org/abs/2607.13649

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