Adaptive Variation-Resilient Random Number Generator for Embedded Encryption

TL;DR

This work presents an adaptive variation-resilient RNG capable of extracting unbiased encryption-grade random number streams from physically driven entropy sources, for embedded cryptography applications and shows consistent operation across a wide range of throughputs from 5 to 182 Mbps.

Abstract

With a growing interest in securing user data within the internet-of-things (IoT), embedded encryption has become of paramount importance, requiring light-weight high-quality Random Number Generators (RNGs). Emerging stochastic device technologies produce random numbers from stochastic physical processes at high quality, however, their generated random number streams are adversely affected by process and supply voltage variations, which can lead to bias in the generated streams. In this work, we present an adaptive variation-resilient RNG capable of extracting unbiased encryption-grade random number streams from physically driven entropy sources, for embedded cryptography applications. The system's key feature is its adaptive digitizer with an adaptive reference voltage. As a proof of concept, we employ a stochastic magnetic tunnel junction (sMTJ) device as an entropy source. The impact of variations in the sMTJ is mitigated by the adaptive digitizer, which generates an adaptive short-term average reference voltage that dynamically tracks any stochastic signal drift or deviation, leading to unbiased random bit stream generation. The generated bit streams, due to their higher entropy, then only need to undergo simplified post-processing. A prototype of the adaptive RNG system was experimentally implemented using discrete electronic components and an FPGA for entropy source emulation. Statistical randomness tests based on the National Institute of Standards and Technology (NIST) test suite are conducted on bit streams obtained using the simulations as well as the discrete electronic component implementation, demonstrating that the bit streams consistently pass all 16 tests of the NIST SP 800-22 test suite with a 100% pass rate. Leveraging its simplified post-processing, the adaptive RNG shows consistent operation across a wide range of throughputs from 5 to 182 Mbps.

Figures (8)

• Figure 1: The adaptive RNG comprising (a) an entropy source, (b) an adaptive digitizer with a low-pass-filter generated moving reference voltage, and (c) compact post processing.
• Figure 2: Comparison between conventional TRNGs and the proposed adaptive TRNG. Illustration of magnetization dynamics in stochastic MTJs for (a) low-barrier magnets and (c) isotropic magnets. The uniform probability distribution of the magnetization in the vertical z-direction ($m_{z}$(t)) depicts characteristics of isotropic sMTJs (inset of (c)). Schematic block diagram Illustration of (b) a conventional TRNG system and (d) the proposed adaptive TRNG system with the adaptive digitizer and the Mini Trivium compact post-processing blocks.
• Figure 3: Simulation results for an sMTJ-based adaptive TRNG. Plots of (a) $V_{Stochastic}$ vs. time, (b) $V_{Digitizer}$ vs. time, and (c) the generated random key stream vs. time, for the conventional TRNG. Plots of (d) $V_{Stochastic}$ vs. time, (e) $V_{Digitizer}$ vs. time and (f) the generated random key stream vs. time, for the adaptive TRNG.
• Figure 4: Process-induced and supply-voltage variation analysis for the proposed adaptive TRNG. (a) Ranges of operation with resilience to variation in $V_{DD}$, $G_0$ and $TMR$. (b) Probability of 1’s and 0’s for: varying $V_{DD}$, varying $G_{0}$, and varying $TMR$, from left to right respectively. (c) Resilience range enhancement across the three parameters, $V_{DD}$, $G_0$ and $TMR$, normalized to the resilience range of the conventional design. (d) 2D pattern of a sample generated random bit stream using the presented adaptive TRNG.
• Figure 5: A scatter plot as a visual illustration of the NIST Test Suite results for the variation analysis. Average P-values across the resilience range to variation in $V_{DD}$, $G_0$, $TMR$ and $\tau_{c}$ for the adaptive TRNG.