2 research outputs found
TuRaN: True Random Number Generation Using Supply Voltage Underscaling in SRAMs
Prior works propose SRAM-based TRNGs that extract entropy from SRAM arrays.
SRAM arrays are widely used in a majority of specialized or general-purpose
chips that perform the computation to store data inside the chip. Thus,
SRAM-based TRNGs present a low-cost alternative to dedicated hardware TRNGs.
However, existing SRAM-based TRNGs suffer from 1) low TRNG throughput, 2) high
energy consumption, 3) high TRNG latency, and 4) the inability to generate true
random numbers continuously, which limits the application space of SRAM-based
TRNGs. Our goal in this paper is to design an SRAM-based TRNG that overcomes
these four key limitations and thus, extends the application space of
SRAM-based TRNGs. To this end, we propose TuRaN, a new high-throughput,
energy-efficient, and low-latency SRAM-based TRNG that can sustain continuous
operation. TuRaN leverages the key observation that accessing SRAM cells
results in random access failures when the supply voltage is reduced below the
manufacturer-recommended supply voltage. TuRaN generates random numbers at high
throughput by repeatedly accessing SRAM cells with reduced supply voltage and
post-processing the resulting random faults using the SHA-256 hash function. To
demonstrate the feasibility of TuRaN, we conduct SPICE simulations on different
process nodes and analyze the potential of access failure for use as an entropy
source. We verify and support our simulation results by conducting real-world
experiments on two commercial off-the-shelf FPGA boards. We evaluate the
quality of the random numbers generated by TuRaN using the widely-adopted NIST
standard randomness tests and observe that TuRaN passes all tests. TuRaN
generates true random numbers with (i) an average (maximum) throughput of
1.6Gbps (1.812Gbps), (ii) 0.11nJ/bit energy consumption, and (iii) 278.46us
latency