A General Purpose Hyperdimensional Computing Accelerator for Edge Computing
Bibliographic record
Abstract
Hyperdimensional computing (HDC) is a lightweight machine learning paradigm. Since HDC relies on bitwise operations instead of matrix multiplications, it is commonly used for classification tasks in edge computing devices. For this purpose, numerous hardware architectures have been proposed to accelerate HDC applications. However, existing solutions suffer from a lack of flexibility, which prevents from a deployment of HDC for a wide range of applications. In this paper, we propose a general-purpose HDC accelerator, called GP-HDCA, which is suitable for FPGAs implementation. To enable the efficient implementation of encoders, which is the most critical component in HDC, we define an instruction set tailored to ease the use of the accelerator as a coprocessor. Synthesis results show that our accelerator, configured with a 32-bit integer size and 32-bit vector slice, requires only 7% of the resources available in a Zedboard. Finally, our results show that a 12x speedup is achieved when processing a language detection application, demonstrating the suitability of the architecture for edge computing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".