A low-cost and flexible FPGA implementation for SPECK block Cipher
Bibliographic record
Abstract
Field Programmable Gate Arrays (FPGAs) are quickly becoming a fundamental flexible integrated circuit building block of choice for many applications such as aerospace, military and defense systems. In addition, instead of using a large number of logic gates, FPGA today can be used to implement any circuits that you want. Furthermore, FPGAs can be configured as system on a chip (SoC). In June 2013 American National Security Agency (NSA) proposed a new block cipher family named SPECK. In this paper, two methods are used to implement this algorithm. First method is used high level synthesis for give more flexibility and to achieve suitable throughput in our implementation. Second method is used bit serialized architecture to achieve minimum area and cost in our design. In second methodology, we have implemented SPECK, with a very small hardware architecture only costs 34 slices and 68 LUTs on a Spartan-3 FPGA. The results show that with a tantamount security level, SPECK is 85% smaller than AES, 68% smaller than PRESENT (a standardized low-cost AES Superseded and an ISO lightweight algorithm).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.004 | 0.001 |
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".