Design and Implementation of a Low-Power Memristor-Based Piccolo-80 Lightweight Encryption Algorithm Using VTM Logic Gates
Bibliographic record
Abstract
Lightweight cryptography (LWC) has become increasingly critical for ensuring secure communication in energy-constrained Internet of Things (IoT) systems. Memristor-based architecture provides a promising approach for secure communication in energy-sensitive and hardware-constrained applications. Piccolo is a lightweight encryption algorithm that offers high security while enabling compact hardware implementation. In addition, Piccolo is specifically designed to operate efficiently in resource-limited environments, making it a strong candidate for low-energy applications such as IoT devices. However, earlier implementations of the Piccolo algorithm on field-programmable gate array (FPGA) platforms, CMOS, and hybrid memristor-CMOS (MeMOS) technology have faced challenges with high power consumption, hardware overhead, and limited scalability. This article presents a novel architecture for implementing the Piccolo-80 encryption algorithm using the voltage-to-memristance (VTM) approach, in which the design maps Piccolo’s primary operations onto VTM stateful logic gates. This enhances performance, reduces switching activity, and leverages the nonvolatile properties of memristors. The proposed design introduces VTM-based memristor logic gates that significantly reduce hardware complexity and power consumption compared with previous implementations. The results from comparing CMOS and hybrid MeMOS implementations in terms of area and energy consumption demonstrate that hardware implementation of Piccolo’s lightweight algorithm using the VTM approach not only improves energy efficiency but also enables the design of optimized, low-power circuits. The design achieves a power consumption of 17.4 mW at 1.8 V and 133 MHz, with only 1214 gate equivalents (GEs), reducing power by up to 32% and area by nearly 20% compared with state-of-the-art hybrid MeMOS designs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".