Gpu and asic acceleration of elliptic curve scalar point multiplication
Bibliographic record
Abstract
As public information is increasingly communicated across public networks such as the internet, the use of public key cryptography to provide security services such as authentication, data integrity, and non-repudiation is ever-growing. Elliptic curve cryptography is being used now more than ever to fulfill the need for public key cryptography, as it provides security equivalent in strength to the entrenched RSA cryptography algorithm, but with much smaller key sizes and reduced computational cost. All elliptic curve cryptography operations rely on elliptic curve scalar point multiplication. In turn, scalar point multiplication depends heavily on finite field multiplication. In this dissertation, two major approaches are taken to accelerate the performance of scalar point multiplication. First, a series of very high performance finite field multiplier architectures have been implemented using domino logic in a CMOS process. Simulation results show that the proposed implementations are more efficient than similar designs in the literature when considering area and delay as performance metrics. The proposed implementations are suitable for integration with a CPU in order to provide a special-purpose finite field multiplication instruction useful for accelerating scalar point multiplication. The next major part of this thesis focuses on the use of consumer computer graphics cards to directly accelerate scalar point multiplication. A number of finite field multiplication algorithms suitable for graphics cards are developed, along with algorithms for finite field addition, subtraction, squaring, and inversion. The proposed graphics-card finite field arithmetic library is used to accelerate elliptic curve scalar point multiplication. The operation throughput and latency performance of the proposed implementation is characterized by a series of tests, and results are compared to the state of the art. Finally, it is shown that graphics cards can be used to significantly increase the operation throughput of scalar point multiplication operations, which makes their use viable for improving elliptic curve cryptography performance in a high-demand server environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".