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Prior works propose SRAM-based TRNGs that extract entropy from SRAM arrays.SRAM arrays are widely used in a majority of specialized or general-purposechips 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) highenergy consumption, 3) high TRNG latency, and 4) the inability to generate truerandom numbers continuously, which limits the application space of SRAM-basedTRNGs. Our goal in this paper is to design an SRAM-based TRNG that overcomesthese four key limitations and thus, extends the application space ofSRAM-based TRNGs. To this end, we propose TuRaN, a new high-throughput,energy-efficient, and low-latency SRAM-based TRNG that can sustain continuousoperation. TuRaN leverages the key observation that accessing SRAM cellsresults in random access failures when the supply voltage is reduced below themanufacturer-recommended supply voltage. TuRaN generates random numbers at highthroughput by repeatedly accessing SRAM cells with reduced supply voltage andpost-processing the resulting random faults using the SHA-256 hash function. Todemonstrate the feasibility of TuRaN, we conduct SPICE simulations on differentprocess nodes and analyze the potential of access failure for use as an entropysource. We verify and support our simulation results by conducting real-worldexperiments on two commercial off-the-shelf FPGA boards. We evaluate thequality of the random numbers generated by TuRaN using the widely-adopted NISTstandard randomness tests and observe that TuRaN passes all tests. TuRaNgenerates true random numbers with (i) an average (maximum) throughput of1.6Gbps (1.812Gbps), (ii) 0.11nJ/bit energy consumption, and (iii) 278.46uslatency.

TuRaN: True Random Number Generation Using Supply Voltage Underscaling in SRAMs