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Record W3036194274

Area and Energy Optimizations in ASIC Implementations of AES and PRESENT Block Ciphers

2020· dissertation· en· W3036194274 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUWSpace (University of Waterloo) · 2020
Typedissertation
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsApplication-specific integrated circuitBlock cipherAdvanced Encryption StandardComputer scienceS-boxImplementationBlock (permutation group theory)AES implementationsParallel computingEmbedded systemArithmeticCryptographyMathematicsAlgorithmProgramming language
DOInot available

Abstract

fetched live from OpenAlex

When small, modern-day devices surface with neoteric features and promise benefits like streamlined business processes, cashierless stores, and autonomous driving, they are all too often accompanied by security risks due to a weak or absent security component. In particular, the lack of data privacy protection is a common concern that can be remedied by implementing encryption. This ensures that data remains undisclosed to unauthorized parties. While having a cryptographic module is often a goal, it is sometimes forfeited because a device's resources do not allow for the conventional cryptographic solutions. Thus, smaller, lower-energy security modules are in demand. Implementing a cipher in hardware as an application-specific integrated circuit (ASIC) will usually achieve better efficiency than alternatives like FPGAs or software, and can help towards goals such as extended battery life and smaller area footprint. The Advanced Encryption Standard (AES) is a block cipher established by the National Institute of Standards and Technology (NIST) in 2001. It has since become the most widely adopted block cipher and is applied in a variety of applications ranging from smartphones to passive RFID tags to high performance microprocessors. PRESENT, published in 2007, is a smaller lightweight block cipher designed for low-power applications. In this study, low-area and low-energy optimizations in ASICs are addressed for AES and PRESENT. In the low-area work, three existing AES encryption cores are implemented, analyzed, and benchmarked using a common fabrication technology (STM 65 nm). The analysis includes an examination of various implementations of internal AES operations and their suitability for different architectural choices. Using our taxonomy of design choices, we designed Quark-AES, a novel 8-bit AES architecture. At 1960 GE, it features a 13% improvement in area and 9% improvement in throughput/area² over the prior smallest design. To illustrate the extent of the variations due to the use of different ASIC libraries, Quark-AES and the three analyzed designs are also synthesized using three additional technologies. Even for the same transistor size, different ASIC libraries produce significantly different area results. To accommodate a variety of applications that seek different levels of tradeoffs in area and throughput, we extend all four designs to 16-bit and 32-bit datawidths. In the low-energy work, round unrolling and glitch filtering are applied together to achieve energy savings. Round unrolling, which applies multiple block cipher rounds in a combinational path, reduces the energy due to registers but increases the glitching energy. Glitch filtering complements round unrolling by reducing the amount of glitches and their associated energy consumption. For unrolled designs of PRESENT and AES, two glitch filtering schemes are assessed. One method uses AND-gates in between combinational rounds while the other used latches. Both methods work by allowing the propagation of signals only after they have stabilized. The experiments assess how energy consumption changes with respect to the degree of unrolling, the glitch filtering scheme, the degree of pipelining, the spacing between glitch filters, and the location of glitch filters when only a limited number of them can be applied due to area constraints. While in PRESENT, the optimal configuration depends on all the variables, in a larger cipher such as AES, the latch-based method consistently offers the most energy savings.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.221
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it