A memristor pulsed on the microsecond scale answers unrepeatably. Lomare's circuit turns that answer into a tunable distribution generator — the sampler that probabilistic compute has been imitating in software.
A pulse source stresses an array of memristors. An analogue conditioning stage differentiates and thresholds the response. A digital Monte-Carlo engine weighs the samples. Nothing in the path computes a random number — it reads one.
Each crosspoint is one transistor and one memristor — a 1T1R cell. Rows carry the pulse; columns read the current. M hardware generators sample in parallel, one per parameter in the model, and every one of them is a different device with a different distribution.
A large pulse deforms the device's internal structure; a small pulse reads what it became. Between 1 and 100 µs that read is irreproducible. The circuit never uses the value itself — it uses the difference between two consecutive reads, which cancels the slow drift and amplifies the randomness.
A sample-and-hold keeps the previous read. Subtracting it is a high-pass filter: the wander of the device disappears, the noise survives. A comparator turns the difference into a bit, and a low-pass of the bit stream feeds back as the threshold — a closed loop that holds the mean where you set it.
The feedback parameters set the mean and the variance of the output. Three parameter sets, three distributions, one device — and no arithmetic anywhere in the loop. This is the proposal distribution a Monte-Carlo method needs, produced by physics.
Prior samples come off the array. A digital stage weights each by the likelihood of the observed data, and the posterior expectation is a weighted sum. Importance sampling in hardware — the expensive part done by a device, the cheap part by logic.
A hardware distribution generator fits wherever randomness is on the critical path: sampling-heavy inference, dither, keys, and the always-on sensing that today's silicon can only duty-cycle.
Proposal samples from the array, importance weights in logic — posterior updates without the PRNG, the floating-point, or the memory traffic.
A drift-free analogue random signal from one memristor and a comparator, stable enough to bias and tune.
True random bits from device physics, differentiated to remove drift, tunable to a target mean.
A hardware-efficient primitive for stochastic computing: one cell, one comparator, one stream.
Continuous belief instead of duty-cycled classification — sampling that costs what a pulse costs.
Array dither generated in place, per device, per pulse.
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