EVMx: An FPGA-Based Accelerator for Smart Contract Processing
Bibliographic record
Abstract
Ethereum leverages smart contracts (SCs) to power decentralized applications (dApps), with execution handled by the Ethereum virtual machine (EVM) within an Ethereum client. Other blockchain platforms, including Avalanche, Polkadot, Aurora, and Cardano, have also adopted the EVM. However, the performance of the EVM is often constrained by the limitations of general-purpose processors, a challenge that has been explored in the literature. This work aims to further address the limitation by proposing EVMx, a dedicated single-core SC execution engine implemented on a field programmable gate array (FPGA). EVMx follows a processor-like architecture inspired by the RISC philosophy. By exploiting the parallelism and high-speed processing capabilities of FPGA hardware, EVMx achieves a 61% to 99% reduction in execution time for commonly used operation codes compared to traditional central processing unit (CPU)-based environments. Furthermore, EVMx executes entire Ethereum blocks with a percentage reduction in execution time between 6% and 56% against comparable FPGA implementations and 98% to 99% compared to CPU-based EVMs in the literature. These results demonstrate the potential of EVMx to significantly accelerate SC execution and enhance the performance of EVM-compatible blockchains.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".