Vector similarity search is a critical but resource-intensive component of Retrieval-Augmented Generation (RAG) pipelines, especially on edge devices. We address this challenge with a highly optimized two-step flat scan primitive using Single Instruction, Multiple Data (SIMD) instructions (AVX2 on x86, NEON on ARM). The first step uses a bandwidth-efficient binary sketch to prefilter candidates, and the second step applies a precise, SIMD-accelerated rescoring to refine the final ranking. We evaluate this primitive against multiple quantization and dimensionality reduction techniques, analyzing trade-offs in latency, accuracy, memory, and energy. On a PC with a 1.2 million vector dataset, our method achieves more than 120 × speedup over a non-SIMD float32 baseline while maintaining an NDCG@100 score of 0.99. On a Raspberry Pi 3 with a 60,000-vector subset, the same method reduces query latency by 39 × ∼ 59 × and energy consumption by 41 × against the same baseline, while achieving an NDCG@100 of 0.98. Our results demonstrate that effective flat scan optimization can deliver substantial performance and energy efficiency gains despite constrained memory bandwidth and limited compute resources.
Simon et al. (2026) studied this question.