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原文摘要

We present a fabricated SRAM DCIM Ising machine that performs set-level context selection for large language model (LLM) with retrieval-augmented generation (RAG). In RAG, the evidence-selection stage is increasingly delegated to repeated language-model reranking, which adds substantial energy to the inference pipeline. We reformulate evidence selection as a quadratic unconstrained binary optimization (QUBO) problem and solve it on a dual-mode digital compute-in-memory (DCIM) macro: the same multiply-and-accumulate datapath computes the embedding-similarity scores that populate the QUBO coefficients and evaluates the per-spin Hamiltonian differences that drive the anneal. The annealing temperature is set by a digital plane-count control along with the SRAM pseudo-read random bit flips, giving graded cooling entirely from on-chip digital configuration. The macro is taped-out in a 28 nm process [0.16 mm2, 0.41 mW] and is proposed to be integrated as part of the logic base-die in hybrid high-bandwidth memory (HBM) and high-bandwidth flash (HBF) stacks in a GPU package, where only the selected passage identifiers return to the host. Replacing the language-model selection pass with the near-memory Ising solver yields a macro normalized selection-energy estimate more than six orders of magnitude lower than the LLM-based baseline and reduces the evaluated total energy per query by 67%, while maintaining comparable token-level F1 on the evaluated examples.