  Stochasticity is ubiquitous in the world around us. However, our predominant
computing paradigm is deterministic. Random number generation (RNG) can be a
computationally inefficient operation in this system especially for larger
workloads. Our work leverages the underlying physics of emerging devices to
develop probabilistic neural circuits for RNGs from a given distribution.
However, codesign for novel circuits and systems that leverage inherent device
stochasticity is a hard problem. This is mostly due to the large design space
and complexity of doing so. It requires concurrent input from multiple areas in
the design stack from algorithms, architectures, circuits, to devices. In this
paper, we present examples of optimal circuits developed leveraging AI-enhanced
codesign techniques using constraints from emerging devices and algorithms. Our
AI-enhanced codesign approach accelerated design and enabled interactions
between experts from different areas of the microelectronics design stack
including theory, algorithms, circuits, and devices. We demonstrate optimal
probabilistic neural circuits using magnetic tunnel junction and tunnel diode
devices that generate an RNG from a given distribution.
