STARC: Selective Token Access with Remapping and Clustering for Efficient LLM Decoding on PIM Systems

Serving large language models (LLMs) places significant pressure on memory systems due to frequent accesses and growing key–value (KV) caches as context lengths increase. Processing-in-memory (PIM) architectures offer high internal bandwidth and near-data compute parallelism, but current designs target dense attention and perform poorly under the irregular access patterns of dynamic KV cache sparsity. To mitigate this limitation, we propose STARC, a sparsity-optimized data mapping scheme for efficient LLM decoding on PIM. STARC clusters semantically similar KV pairs and co-locates them contiguously within PIM banks, enabling retrieval at cluster granularity by matching queries against precomputed centroids. This bridges the gap between fine-grained sparse attention and row-level PIM operations, improving utilization while minimizing overhead. On a simulated HBM-PIM system, under constrained KV budgets, STARC achieves up to 78% and 65% reductions in attention-layer latency and energy over token-wise sparsity methods, and up to 93% and 92% reductions relative to full attention, while preserving model accuracy.