Aperon's HNTL speeds up vector search 3.61x with pointerless design
New indexing method eliminates pointer overhead, achieving 3.61x faster search with near-zero cache misses.
Traditional approximate nearest neighbor (ANN) search relies on proximity graphs like HNSW, which suffer from a heavy 'pointer tax'—high memory overhead due to storing adjacency pointers and irregular memory accesses that stall CPU pipelines. Aperon's new technical report presents HNTL (Hierarchical No-pointer Tangent-Local), a core component of the Aperon vector memory system that eliminates pointers entirely. HNTL partitions high-dimensional space into coherent 'grains', projects vectors onto local tangent spaces using PCA, and stores them in a pointerless Block-SoA (Structure-of-Arrays) layout. This design enables sequential memory scans that leverage auto-vectorization (NEON on ARM), dramatically improving throughput.
On anisotropic manifold data (d=768, N=10,000), local PCA captures 96.3% of the variance, allowing HNTL to achieve a Rerank Recall@10 of 1.0 with only C=20 candidate vectors. Hardware profiling using Apple kperf PMU counters shows a 3.61x speedup (4.137 ns/vector vs 14.951 ns/vector) over pointer-chasing graph traversals, driven by a 3.59x IPC increase and near-zero L1/L2 data cache misses. This breakthrough could reshape high-dimensional ANN search in databases, ML pipelines, and vector retrieval systems, offering faster and more memory-efficient alternatives to HNSW and similar graph-based methods.
- HNTL replaces pointer-heavy proximity graphs with a pointerless Block-SoA layout, achieving 3.61x speedup (4.137 ns/vector vs 14.951 ns/vector) on Apple CPUs with NEON auto-vectorization.
- Local PCA captures 96.3% of variance on anisotropic manifold data (d=768, N=10,000), enabling perfect Rerank Recall@10 with only C=20 candidate vectors.
- Hardware profiling via Apple kperf shows 3.59x IPC improvement and near-zero L1/L2 cache misses, eliminating the pointer tax and pipeline stalls.
Why It Matters
Faster, memory-efficient vector search accelerates recommendation systems, similarity search, and AI retrieval pipelines.