64 research outputs found
Autonomous Probabilistic Coprocessing with Petaflips per Second
In this paper we present a concrete design for a probabilistic (p-) computer
based on a network of p-bits, robust classical entities fluctuating between -1
and +1, with probabilities that are controlled through an input constructed
from the outputs of other p-bits. The architecture of this probabilistic
computer is similar to a stochastic neural network with the p-bit playing the
role of a binary stochastic neuron, but with one key difference: there is no
sequencer used to enforce an ordering of p-bit updates, as is typically
required. Instead, we explore \textit{sequencerless} designs where all p-bits
are allowed to flip autonomously and demonstrate that such designs can allow
ultrafast operation unconstrained by available clock speeds without
compromising the solution's fidelity. Based on experimental results from a
hardware benchmark of the autonomous design and benchmarked device models, we
project that a nanomagnetic implementation can scale to achieve petaflips per
second with millions of neurons. A key contribution of this paper is the focus
on a hardware metric flips per second as a problem and
substrate-independent figure-of-merit for an emerging class of hardware
annealers known as Ising Machines. Much like the shrinking feature sizes of
transistors that have continually driven Moore's Law, we believe that flips per
second can be continually improved in later technology generations of a wide
class of probabilistic, domain specific hardware.Comment: 13 pages, 8 figures, 1 tabl
Adiabatic evolution on a spatial-photonic Ising machine
Combinatorial optimization problems are crucial for widespread applications
but remain difficult to solve on a large scale with conventional hardware.
Novel optical platforms, known as coherent or photonic Ising machines, are
attracting considerable attention as accelerators on optimization tasks
formulable as Ising models. Annealing is a well-known technique based on
adiabatic evolution for finding optimal solutions in classical and quantum
systems made by atoms, electrons, or photons. Although various Ising machines
employ annealing in some form, adiabatic computing on optical settings has been
only partially investigated. Here, we realize the adiabatic evolution of
frustrated Ising models with 100 spins programmed by spatial light modulation.
We use holographic and optical control to change the spin couplings
adiabatically, and exploit experimental noise to explore the energy landscape.
Annealing enhances the convergence to the Ising ground state and allows to find
the problem solution with probability close to unity. Our results demonstrate a
photonic scheme for combinatorial optimization in analogy with adiabatic
quantum algorithms and enforced by optical vector-matrix multiplications and
scalable photonic technology.Comment: 9 pages, 4 figure
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