Segmented Angular Pre-Processing for Accurate and Efficient In-Memory Vector Similarity Search

Vector similarity search (VSS) is a fundamental operation in modern AI applications, including few-shot learning (FSL) and approximate nearest neighbor search (ANNS). Cosine similarity is widely regarded as the optimal metric for VSS. However, VSS incurs substantial energy and computational overhead, primarily due to frequent vector transfers and the complexity of cosine similarity calculations in high-dimensional spaces. Prior research has explored the use of ternary content addressable memories (TCAMs) for parallel in-memory VSS to reduce vector movement. Exact-Match TCAM (EX-TCAM) enables exact bitmatching, and Best-Match TCAM (Best-TCAM) supports Hamming distance calculations, both of which are spatial metrics and computationally efficient. As a result, existing TCAM-based VSS approaches have focused on developing frameworks to efficiently support more complex spatial metrics such as the $L_{\infty}$ and $L_{1}$ norms. However, these spatial metrics exhibit notable discrepancies compared to angular metrics like cosine similarity. To overcome this limitation, we propose Seg-Cos, a TCAM-based framework that directly approximates cosine similarity within TCAM for angular VSS. Seg-Cos introduces a dedicated preprocessing technique and encoding scheme that segments vectors and encodes them as circular ranges based on their angles and magnitudes. Seg-Cos is the first angular VSS framework compatible with both EX-TCAM and Best-TCAM, enabling accurate and energy-efficient VSS in the angular domain. Simulation results demonstrate that Seg-Cos improves energy efficiency by $1.41 \times$ and achieves up to $2.2 \%$ higher accuracy over prior EX-TCAMbased methods in FSL. In ANNS, Seg-Cos enhances recall rate by $10 \%$ to $52 \%$ and improves energy efficiency by $2 \times$ compared to previous Best-TCAM approaches with $L_{1}$ norm.