A5001666662- Quantum Computing Algorithms and Architecture
- Advanced Memory and Neural Computing
- Magnetic properties of thin films
- Quantum and electron transport phenomena
- Ferroelectric and Negative Capacitance Devices
- Neural Networks and Reservoir Computing
- Neural Networks and Applications
- Error Correcting Code Techniques
- Low-power high-performance VLSI design
- Quantum-Dot Cellular Automata
- Stochastic Gradient Optimization Techniques
- Advancements in Semiconductor Devices and Circuit Design
- Surface and Thin Film Phenomena
- Computability, Logic, AI Algorithms
- Physics of Superconductivity and Magnetism
- Magnetic Field Sensors Techniques
- DNA and Biological Computing
- Theoretical and Computational Physics
- Machine Learning in Materials Science
- Multiferroics and related materials
- Ferroelectric and Piezoelectric Materials
- Neural dynamics and brain function
- Quantum Information and Cryptography
- Machine Learning and Algorithms
- Generative Adversarial Networks and Image Synthesis
University of California, Santa Barbara
2020-2025
Purdue University West Lafayette
2013-2020
Institute of Nanotechnology
2016
We introduce the concept of a probabilistic or p-bit, intermediate between standard bits digital electronics and emerging q-bits quantum computing. show that low barrier magnets LBM's provide natural physical representation for p-bits can be built either from perpendicular (PMA) designed to close in-plane transition circular (IMA). Magnetic tunnel junctions (MTJ) using as free layers combined with NMOS transistors three-terminal building blocks large scale circuits perform useful functions....
The transistor celebrated its 75 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> birthday in 2022. continued scaling of the defined by Moore's Law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to transistors for general-purpose computing, integration with unconventional technologies has emerged as...
Abstract In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At same time, adopting a variety of nanotechnologies offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for roadmap to guide future research, this collection aims fill that need. authors provide comprehensive neuromorphic using electron spins,...
Abstract Extending Moore’s law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based bits (p-bits) Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X =...
Magnetic tunnel junctions (MTJs) utilizing unstable magnets with low barriers have been shown to be well-suited for the implementation of random number generators (RNGs). It has recently that completely new applications involving optimization, inference, and invertible Boolean logic would enabled if many RNGs can interconnected form large scale correlated networks. However, this requires a device, namely, three-terminal tunable RNG or p-bit, whose input terminal used pin its output 0 1. In...
Conventional logic and memory devices are built out of deterministic units such as transistors, or magnets with energy barriers in excess 40-60 kT. We show that stochastic units, p-bits, can be interconnected to create robust correlations implement Boolean functions impressive accuracy, comparable standard circuits. Also they invertible, a unique property is absent digital When operated the direct mode, input clamped, network provides correct output. In inverted output fluctuates among...
Abstract This paper draws attention to a hardware system which can be engineered so that its intrinsic physics is described by the generalized Ising model and encode solution many important NP-hard problems as ground state. The basic constituents are stochastic nanomagnets switch randomly between ±1 states monitored continuously with standard electronics. Their mutual interactions short or long range, their strengths reconfigured needed solve specific anneal at room temperature. natural laws...
There has been enormous progress in the last two decades, effectively combining spintronics and magnetics into a powerful force that is shaping field of memory devices. New materials phenomena continue to be discovered at an impressive rate, providing ever-increasing set building blocks could exploited designing transistor-like functional devices future. The objective this paper provide quantitative foundation for block approach, so new discoveries can integrated device concepts, quickly...
By its nature, a conventional computer based on deterministic bits is ill-matched to tasks such as sampling, inference, and optimization. A $p\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}b\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}b\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}l\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}s\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}c$ can be natural tool for problems which are at base...
Binary stochastic neurons (BSN's) form an integral part of many machine learning algorithms, motivating the development hardware accelerators for this complex function. It has been recognized that BSN's can be implemented using low barrier magnets (LBM's) by minimally modifying present-day magnetoresistive random access memory (MRAM) devices. A crucial parameter determines response these LBM based BSN designs is \emph{correlation time} magnetization, $\tau_c$. In letter, we show with energy...
In this paper we present a concrete design for probabilistic (p-) computer based on network of p-bits, robust classical entities fluctuating between -1 and +1, with probabilities that are controlled through an input constructed from the outputs other p-bits. The architecture is similar to stochastic neural p-bit playing role binary neuron, but one key difference: there no sequencer used enforce ordering updates, as typically required. Instead, explore \textit{sequencerless} designs where all...
Quantum Monte Carlo (QMC) techniques are widely used in a variety of scientific problems and much work has been dedicated to developing optimized algorithms that can accelerate QMC on standard processors (CPU). With the advent various special purpose devices domain specific hardware, it become increasingly important establish clear benchmarks what improvements these technologies offer compared existing technologies. In this paper, we demonstrate 2 3 orders magnitude acceleration algorithm...
The common feature of nearly all logic and memory devices is that they make use stable units to represent 0's 1's. A completely different paradigm based on three-terminal stochastic which could be called "p-bits", where the output a random telegraphic signal continuously fluctuating between 0 1 with tunable mean. p-bits can interconnected receive weighted contributions from others in network, these chosen not only solve problems optimization inference but also implement precise Boolean...
Probabilistic spin logic is a recently proposed computing paradigm based on unstable stochastic units called probabilistic bits (p-bits) that can be correlated to form circuits (p-circuits). These p-circuits used solve the problems of optimization, inference, and implement precise Boolean functions in an "inverted" mode, where given circuit operate reverse find input combinations are consistent with output. In this brief, we present scalable field-programmable gate array implementation such...
We present here that nanomagnet networks provide a natural hardware platform for performing probabilistic computing in the Ising framework. By using network of weakly coupled nanomagnets, we experimentally demonstrate first time convergence network's magnetization towards ground state associated Hamiltonian. The experimental results are excellent agreement with model and stochastic magnet dynamics simulations.
Magnetoresistive random access memory (MRAM) technologies with thermally unstable nanomagnets are leveraged to develop an intrinsic stochastic neuron as a building block for restricted Boltzmann machines (RBMs) form deep belief networks (DBNs). The embedded MRAM-based is modeled using precise physics equations. simulation results exhibit the desired sigmoidal relation between input voltages and probability of output state. A probabilistic inference network simulator (PIN-Sim) developed...
The valley degree of freedom electrons in two-dimensional transition metal dichalcogenides has been extensively studied by theory (1-4), optical (5-9), and optoelectronic (10-13) experiments. However, generation detection pure current without relying on selection have not yet demonstrated these materials. Here, we report that can be electrically induced detected through the Hall effect inverse effect, respectively, monolayer molybdenum disulfide. We compare temperature channel length...
The commercial and industrial demand for the solution of hard combinatorial optimization problems push forward development efficient solvers. One them is Ising machine which can solve mapped to Hamiltonians. In particular, spintronic hardware implementations machines be very in terms area performance, are relatively low-cost considering potential create hybrid CMOS-spintronic technology. Here, we perform a comparison coherent probabilistic paradigms on several Max-Cut instances, analyzing...
It has recently been shown that a suitably interconnected network of tunable telegraphic noise generators or "p-bits" can be used to perform even precise arithmetic functions like 32-bit adder. In this letter, we use simulations based on the stochastic Landau–Lifshitz–Gilbert (sLLG) equation demonstrate similar impressive performed using unstable nanomagnets with energy barriers as low fraction kT. This is surprising because magnetization low-barrier not discrete values <inline-formula...
A low-energy hardware implementation of deep belief network (DBN) architecture is developed using near-zero energy barrier probabilistic spin logic devices (p-bits), which are modeled to realize an intrinsic sigmoidal activation function. CMOS/spin based weighted array structure designed implement a restricted Boltzmann machine (RBM). Device-level simulations on precise physics relations used validate the relation between output probability p-bit and its input currents. Characteristics...