5 research outputs found
Physics-inspired Ising Computing with Ring Oscillator Activated p-bits
The nearing end of Moore's Law has been driving the development of
domain-specific hardware tailored to solve a special set of problems. Along
these lines, probabilistic computing with inherently stochastic building blocks
(p-bits) have shown significant promise, particularly in the context of hard
optimization and statistical sampling problems. p-bits have been proposed and
demonstrated in different hardware substrates ranging from small-scale
stochastic magnetic tunnel junctions (sMTJs) in asynchronous architectures to
large-scale CMOS in synchronous architectures. Here, we design and implement a
truly asynchronous and medium-scale p-computer (with 800 p-bits) that
closely emulates the asynchronous dynamics of sMTJs in Field Programmable Gate
Arrays (FPGAs). Using hard instances of the planted Ising glass problem on the
Chimera lattice, we evaluate the performance of the asynchronous architecture
against an ideal, synchronous design that performs parallelized (chromatic)
exact Gibbs sampling. We find that despite the lack of any careful
synchronization, the asynchronous design achieves parallelism with comparable
algorithmic scaling in the ideal, carefully tuned and parallelized synchronous
design. Our results highlight the promise of massively scaled p-computers with
millions of free-running p-bits made out of nanoscale building blocks such as
stochastic magnetic tunnel junctions.Comment: To appear in the 22nd IEEE International Conference on Nanotechnology
(IEEE-NANO 2022
High Electron Mobility Transistors: Performance Analysis, Research Trend and Applications
In recent years, high electron mobility transistors (HEMTs) have received extensive attention for their superior electron transport ensuring high speed and high power applications. HEMT devices are competing with and replacing traditional fieldâeffect transistors (FETs) with excellent performance at high frequency, improved power density and satisfactory efficiency. This chapter provides readers with an overview of the performance of some popular and mostly used HEMT devices. The chapter proceeds with different structures of HEMT followed by working principle with graphical illustrations. Device performance is discussed based on existing literature including both analytical and numerical models. Furthermore, some notable latest research works on HEMT devices have been brought into attention followed by prediction of future trends. Comprehensive knowledge of upâtoâdate results, future directions, and their analysis methodology would be helpful in designing novel HEMT devices
CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning
Extending Moore's law by augmenting complementary-metal-oxide semiconductor
(CMOS) transistors with emerging nanotechnologies (X) has become increasingly
important. Accelerating Monte Carlo algorithms that rely on random sampling
with such CMOS+X technologies could have significant impact on a large number
of fields from probabilistic machine learning, optimization to quantum
simulation. In this paper, we show the combination of stochastic magnetic
tunnel junction (sMTJ)-based probabilistic bits (p-bits) with versatile Field
Programmable Gate Arrays (FPGA) to design a CMOS + X (X = sMTJ) prototype. Our
approach enables high-quality true randomness that is essential for Monte Carlo
based probabilistic sampling and learning. Our heterogeneous computer
successfully performs probabilistic inference and asynchronous Boltzmann
learning, despite device-to-device variations in sMTJs. A comprehensive
comparison using a CMOS predictive process design kit (PDK) reveals that
compact sMTJ-based p-bits replace 10,000 transistors while dissipating two
orders of magnitude of less energy (2 fJ per random bit), compared to digital
CMOS p-bits. Scaled and integrated versions of our CMOS + stochastic nanomagnet
approach can significantly advance probabilistic computing and its applications
in various domains by providing massively parallel and truly random numbers
with extremely high throughput and energy-efficiency
Massively Parallel Probabilistic Computing with Sparse Ising Machines
Inspired by the developments in quantum computing, building domain-specific
classical hardware to solve computationally hard problems has received
increasing attention. Here, by introducing systematic sparsification
techniques, we demonstrate a massively parallel architecture: the sparse Ising
Machine (sIM). Exploiting sparsity, sIM achieves ideal parallelism: its key
figure of merit - flips per second - scales linearly with the number of
probabilistic bits (p-bit) in the system. This makes sIM up to 6 orders of
magnitude faster than a CPU implementing standard Gibbs sampling. Compared to
optimized implementations in TPUs and GPUs, sIM delivers 5-18x speedup in
sampling. In benchmark problems such as integer factorization, sIM can reliably
factor semiprimes up to 32-bits, far larger than previous attempts from D-Wave
and other probabilistic solvers. Strikingly, sIM beats competition-winning SAT
solvers (by 4-700x in runtime to reach 95% accuracy) in solving 3SAT problems.
Even when sampling is made inexact using faster clocks, sIM can find the
correct ground state with further speedup. The problem encoding and
sparsification techniques we introduce can be applied to other Ising Machines
(classical and quantum) and the architecture we present can be used for scaling
the demonstrated 5,000-10,000 p-bits to 1,000,000 or more through analog CMOS
or nanodevices