authorea
Authorea
10.36227/techrxiv.11631933.v1
Electrically Programmable Probabilistic Bit Anti-Correlator on a
Nanomagnetic Platform
McCray
Mason
Abeed
Md Ahsanul
0000-0001-6074-1212
Bandyopadhyay
Supriyo
Virginia Commonwealth University
30
10
2023
This preprint is available at https://doi.org/10.36227/techrxiv.11631933.v1
Probabilistic computing algorithms require electrically
programmable stochasticity to encode arbitrary probability functions and
controlled stochastic interaction or correlation between probabilistic
(p-) bits. The latter is implemented with complex electronic components
leaving a large footprint on a chip and dissipating excessive amount of
energy. Here, we show an elegant implementation with just two
dipole-coupled magneto-tunneling junctions (MTJ), with magnetostrictive
soft layers, fabricated on a piezoelectric film. The resistance states
of the two MTJs (high or low) encode the p-bit values (1 or 0) in the
two streams. The first MTJ is driven to a resistance state with desired
probability via a current or voltage that generates spin transfer
torque, while the second MTJ’s resistance state is determined by dipole
coupling with the first, thus correlating the second p-bit stream with
the first. The effect of dipole coupling can be varied by generating
local strain in the soft layer of the second MTJ with a local voltage
(~ 0.2 V) and that varies the degree of anti-correlation
between the resistance states of the two MTJs and hence between the two
streams (from 0% to 100%). This paradigm generates the
anti-correlation with “wireless” dipole coupling that consumes no
footprint on a chip and dissipates no energy, and it controls the degree
of anti-correlation with electrically generated strain that consumes
minimal footprint and is extremely frugal in its use of energy. It can
be extended to arbitrary number of bit streams. This realizes an
“all-magnetic” platform for probabilistic computing.
components, circuits, devices and systems
computing and processing
low energy computing
magnetic tunnel junctions
stochastic and probabilistic computing