The intrinsic behaviors of spin-transfer-torque magnetoresistive random access memory
(STT-MRAM) are exploited to implement a true random number generator (TRNG) and …

In this paper, we present approaches to generating random numbers, along with potential
applications. Rather than trying to provide extensive coverage of several techniques or …

We demonstrate device codesign using reinforcement learning for probabilistic computing
applications. We use a spin orbit torque magnetic tunnel junction model (SOT-MTJ) as the …

In the field of nuclear science, obtaining and utilizing nuclear data, including nuclear
reaction data, nuclear structure information, and radioactive decay data, is crucial. Neutron …

To meet the demanding requirements of VLSI design, including improved speed, reduced
power consumption, and compact architectures, various IP cores from trusted and untrusted …

In this work, we analyse Commercial Off-The-Shelf (COTS) Resistive Random Access
Memory (ReRAM) modules for their suitability to implement a novel runtime-accessible True …

(Pseudo) random sampling, a costly yet widely used method in (probabilistic) machine
learning and Markov Chain Monte Carlo algorithms, remains unfeasible on a truly large …

Device discovery and circuit modeling for emerging devices, such as magnetic tunnel
junctions, require detailed and time-consuming device and circuit simulations. In this work …

True random number generator (TRNG) is a key block of security applications which need
cryptographic protections. However, most of the conventional TRNGs which use physical …

Biologically-inspired computing models have made significant progress in recent years, but
the conventional von Neumann architecture is inefficient for the large-scale matrix …