Energy-Efficient In-Memory Vector Similarity Search with Fidelity-aware Range Encoding
Journal
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Start Page
1-1
ISSN
0278-0070
1937-4151
Date Issued
2025
Author(s)
Abstract
Vector 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.
Subjects
In-memory-search
range encoding
ternary content-addressable memory
vector similarity search
SDGs
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Type
journal article