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Analog compute-in-memory (CIM) arrays offer energy-efficient LLM inference but face challenges with KV cache updates in attention mechanisms. The paper proposes selective KV cache protection to address noise and dynamic computation mismatches in analog CIM systems.
Analog compute-in-memory (CIM) arrays offer energy-efficient LLM inference but face challenges with KV cache updates in attention mechanisms. The paper proposes selective KV cache protection to address noise and dynamic computation mismatches in analog CIM systems.
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Analog compute-in-memory (CIM) arrays have emerged as a promising substrate for energy-efficient LLM inference, particularly for weight-stationary computations in linear layers. However, extending analog CIM to attention mechanisms introduces a fundamental challenge: KV cache operations demand repeated in-situ...
This approach could improve the reliability and efficiency of analog CIM hardware for LLM inference, especially in handling attention mechanisms that require frequent KV cache updates. Enhancing noise resilience is key to practical deployment of analog CIM in large-scale language models.
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