Huang, Chi-TseChi-TseHuangCheng, Hsiang-YunHsiang-YunChengAN-YEU(ANDY) WU2025-12-122025-12-12202502780070https://www.scopus.com/record/display.uri?eid=2-s2.0-105021657163&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/734569Vector similarity search (VSS) is a crucial operation in applications of machine learning, but it often incurs high energy consumption due to frequent memory accesses. Previous works have adopted ternary content-addressable memories (TCAMs) to perform parallel VSS within memory. Among these approaches, Exact-Match TCAM (EX-TCAM) combined with range encoding has shown promise for executing in-memory VSS under the L∞ norm distance. Existing EX-TCAM-based approaches initiate the search from the positions of query vectors and iteratively expand the search ranges to identify the closest stored vectors. However, this initialization strategy leads to excessive search iterations and long codewords. To address these challenges, we present FORE, an EX-TCAM-based framework that significantly improves latency, energy efficiency, and accuracy for in-memory VSS. To reduce redundant search iterations, we first initialize the search from ranges based on the L∞ norm distance. To shorten the codeword length, we then develop a range encoding scheme that supports range-to-range matching. In addition, we introduce a novel metric, “range fidelity,” to evaluate the quality of range encoding. Building on the insight that a certain degree of loss in range fidelity is tolerable in EX-TCAM-based VSS, we further propose a lossy range encoding scheme that yields more compact codewords without significantly compromising accuracy. Finally, FORE incorporates a L∞ norm distance-based training mechanism that further reduces search iterations and enhances classification accuracy in EX-TCAM-based VSS. Experimental results demonstrate that FORE improves energy efficiency by 45.25× to 59.72× and reduces latency by 4.47× to 5.66× compared to previous EX-TCAM-based methods within 1% accuracy loss. Moreover, FORE outperforms Best-Match TCAM (Best-TCAM)-based approaches by 6.9× in energy efficiency under non-ideal conditions.falseIn-memory-searchrange encodingternary content-addressable memoryvector similarity search[SDGs]SDG7journal article10.1109/tcad.2025.36317412-s2.0-105021657163