Efficient Hyperdimensional Computing Accelerator Design for Chinese Text Classification Task

YU Tianyang; WU Bi; LIU Weiqiang

doi:10.11999/JEIT260556

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YU Tianyang, WU Bi, LIU Weiqiang. Efficient Hyperdimensional Computing Accelerator Design for Chinese Text Classification Task[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260556

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YU Tianyang, WU Bi, LIU Weiqiang. Efficient Hyperdimensional Computing Accelerator Design for Chinese Text Classification Task[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260556

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Efficient Hyperdimensional Computing Accelerator Design for Chinese Text Classification Task

doi: 10.11999/JEIT260556 cstr: 32379.14.JEIT260556

1.
College of Integrated Circuits, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
Key Laboratory of Aerospace Integrated Circuits and Microsystem, Ministry of Industry and Information Technology, Nanjing 211106, China

Funds: The National Natural Science Foundation of China (62425404, 92364201, 62522406), Basic Research Program of Jiangsu (BK20253023)

Accepted Date: 2026-07-28
Rev Recd Date: 2026-07-28

Available Online: 2026-08-07

Abstract

Abstract

Objective With the proliferation of edge computing in IoT, smart wearables, and offline terminals, low-latency and high-privacy Chinese text analysis on local devices has become a core requirement. While neural network-based and Transformer-based language models achieve excellent accuracy, their massive parameter scales and computational demands make them unsuitable for power- and storage-constrained edge devices. Hyperdimensional Computing (HDC), an emerging brain-inspired paradigm, represents text through 2K-10K dimensional hypervectors and employs lightweight encoding-query mechanisms instead of complex multi-layer networks, offering a hardware-friendly approach for edge-side text tasks. However, existing HDC research has focused exclusively on alphabetic writing systems such as English, where a limited set of letters suffices for N-gram encoding. For Chinese, with its thousands of commonly used characters, directly applying existing methods would cause prohibitive storage overhead from the explosion of base hypervectors, fundamentally undermining the lightweight advantage of HDC. This paper aims to overcome this bottleneck by proposing a novel character encoding method and a dedicated HDC framework with hardware acceleration for Chinese text classification. Methods Drawing inspiration from the structural characteristics of Chinese characters as ideographic symbols, this paper proposes a hyperdimensional encoding method based on character glyph structure. Specifically, the method reverse-engineers the encoding process of the Wubi input method, decomposing each Chinese character into its constituent strokes and radicals and mapping them to equivalent letter sequences for efficient hyperdimensional encoding. A retrieval library covering 3,500 commonly used Chinese characters, as defined in the General Standard Chinese Characters Table issued by the Ministry of Education of the People's Republic of China, is constructed. For each character, the Wubi library is queried to obtain a letter sequence of length 3 or 4, where each letter position corresponds to a base hypervector. Through cyclic shift and binding operations applied to the base hypervectors of consecutive letters, the character is encoded into a character-level hypervector that simultaneously captures both letter identity and sequential order information, effectively preventing confusion arising from different letter permutations. This approach reduces the storage overhead associated with base hypervectors by more than 95% compared to the method of directly assigning independent hypervectors to each Chinese character (Table 1). Building upon this character encoding scheme, the HDChinese framework is developed to support both training and inference procedures. During inference, the sentence-level hypervector, obtained by bundling all character hypervectors, queries the class hypervectors via cosine similarity, which is efficiently realized through XNOR-based inner product computations. During training, class hypervectors are derived by bundling sentence hypervectors belonging to the same category and applying a sign-based binarization operation. A subsequent retraining phase iteratively fine-tunes the non-binary class hypervectors using misclassified samples with a learning rate of 0.25, thereby mitigating the influence of outlier hypervectors on the cluster center. Furthermore, a dedicated hardware accelerator architecture tailored to HDChinese is designed. The architecture comprises three main modules: an encoding module, a querying module, and a class hypervector update module, which can be selectively activated to support inference-only, training, and retraining modes respectively (Fig. 2). The accelerator is prototyped on an Ultra96v2 FPGA development board featuring a Xilinx ZYNQ SoC. Results and Discussions Four open-source datasets covering both binary and multi-class tasks are used (Table 2). The Wubi-based encoding outperforms the Pinyin-based method in accuracy while saving 31.46% storage (Table 4), and ignoring rare characters (average frequency below 0.5%) has negligible accuracy impact (Table 4). A dimension of 2K is adopted for hypervectors as higher dimensions yield no significant gains (Fig. 3). FPGA measurements show HDChinese achieves a model size of approximately 16 KB—over 99% smaller than kNN, SVM, and Random Forest (Table 6). Inference time is reduced by 6.67%-47.25% and total training time by 4.91%-77.36% compared to SVM and Random Forest, while accuracy after retraining reaches comparable levels (Fig. 5, Table 6). Additionally, the KB-scale model enables operation without external memory chips, unlike comparison algorithms requiring over 400 MB of runtime memory. At 100 MHz, power consumption is 0.278 W, representing the optimal throughput-power trade-off (Table 5). Conclusions This paper proposes a hyperdimensional encoding method based on Chinese character glyph structure, resolving the incompatibility between existing HDC methods and Chinese text. The HDChinese framework and its dedicated hardware accelerator achieve over 99% model complexity reduction while maintaining competitive accuracy, with training time reductions of 4.91%-77.36% and inference time reductions of 6.67%-47.25%. The proposed approach effectively balances accuracy with extremely low overhead, providing an ideal hardware solution for Chinese text analysis on edge devices.
- Hyperdimensional Computing,
- Edge-side Accelerator,
- Natural Language Processing,
- Chinese Text Classification

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References(23)

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