HyperXArray: Low-Power and Compact Memristive Architecture for In-Memory Encryption on Edge
Why this work is in the frame
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Bibliographic record
Abstract
Encryption on large-scale memristor crossbars proves to be challenging due to the spatial and temporal fluctuations of the signals coming from numerous non-idealities. To address this, we utilize Hyperlock, a memristive vector-matrix multiplication accelerator employing hyperdimensional computing for encryption. We demonstrate that stochasticity generated on TiOx memristor crossbars with passive 0T1R arrangement can be decryptable under the appropriate training of a neural network. We present HyperXArray, an architecture for Hyperlock's encryption scheme, that is capable of weight regeneration, and analog/digital encryption without the need for high-resolution Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). We demonstrate 100% decryption accuracy for digital encryption and show that HyperXArray is capable of encryption during analog to digital conversion that reduces the power consumption of ADC by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$50\times$</tex-math></inline-formula>. In digital encryption, we show that HyperXArray reduces energy consumption by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10\times$</tex-math></inline-formula> and footprint by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10-100\times$</tex-math></inline-formula> compared to Field Programmable Gate Array (FPGA) implementations of Advanced Encryption Standard (AES), while maintaining the same level of throughput. Overall, HyperXArray demonstrates its capability to fill the niche for lightweight, noise-resilient encryption on edge with only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.1mm^{2}$</tex-math></inline-formula> footprint and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$60 pJ/bit$</tex-math></inline-formula> energy efficiency.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it