Silicon-based metal-oxide-semiconductor quantum dots are prominent candidates for high-fidelity, manufacturable qubits. Due to silicon's band structure, additional low-energy states persist in these devices, presenting both challenges and opportunities. Although the physics governing these valley states has been the subject of intense study, quantitative agreement between experiment and theory remains elusive. Here, we present data from an experiment probing the valley states of quantum dot devices and develop a theory that is in quantitative agreement with both this and a recently reported experiment. Through sampling millions of realistic cases of interface roughness, our method provides evidence that the valley physics between the two samples is essentially the same.
We propose a new particle-in-cell (PIC) method for the simulation of plasmas based on a recently developed, unconditionally stable solver for the wave equation. This method is not subject to a CFL restriction, limiting the ratio of the time step size to the spatial step size, typical of explicit methods, while maintaining computational cost and code complexity comparable to such explicit schemes. We describe the implementation in one and two dimensions for both electrostatic and electromagnetic cases, and present the results of several standard test problems, showing good agreement with theory with time step sizes much larger than allowed by typical CFL restrictions.
Here, first-principles molecular dynamics simulations were used to investigate the dissociation of sarin (GB) on the calcium silicate hydrate (CSH) mineral tobermorite (TBM), a surrogate for cement. CSH minerals (including TBM) and amorphous materials of similar composition are the major components of Portland cement, the binding agent of concrete. Metadynamics simulations were used to investigate the effect of the TBM surface and confinement in a microscale pore on the mechanism and free energy of dissociation of GB. Our results indicate that both the adsorption site and the humidity of the local environment significantly affect the sarin dissociation energy. In particular, sarin dissociation in a low-water environment occurs via a dealkylation mechanism, which is consistent with previous experimental studies.
2016 IEEE International Conference on Rebooting Computing, ICRC 2016 - Conference Proceedings
DeBenedictis, Erik; Frank, Michael P.; Ganesh, Natesh; Anderson, Neal G.
At roughly kT energy dissipation per operation, the thermodynamic energy efficiency "limits" of Moore's Law were unimaginably far off in the 1960s. However, current computers operate at only 100-10,000 times this limit, forming an argument that historical rates of efficiency scaling must soon slow. This paper reviews the justification for the ∼kT per operation limit in the context of processors for von Neumann-class computer architectures of the 1960s. We then reapply the fundamental arguments to contemporary applications and identify a new direction for future computing in which the ultimate efficiency limits would be much further out. New nanodevices with high-level functions that aggregate the functionality of several logic gates and some local memory may be the right building blocks for much more energy efficient execution of emerging applications - such as neural networks.
Amidst the rising impact of machine learning and the popularity of deep neural networks, learning theory is not a solved problem. With the emergence of neuromorphic computing as a means of addressing the von Neumann bottleneck, it is not simply a matter of employing existing algorithms on new hardware technology, but rather richer theory is needed to guide advances. In particular, there is a need for a richer understanding of the role of adaptivity in neural learning to provide a foundation upon which architectures and devices may be built. Modern machine learning algorithms lack adaptive learning, in that they are dominated by a costly training phase after which they no longer learn. The brain on the other hand is continuously learning and provides a basis for which new mathematical theories may be developed to greatly enrich the computational capabilities of learning systems. Game theory provides one alternative mathematical perspective analyzing strategic interactions and as such is well suited to learning theory.
Continuing to improve computational energy efficiency will soon require developing and deploying new operational paradigms for computation that circumvent the fundamental thermodynamic limits that apply to conventionally-implemented Boolean logic circuits. In particular, Landauer's principle tells us that irreversible information erasure requires a minimum energy dissipation of kT ln 2 per bit erased, where k is Boltzmann's constant and T is the temperature of the available heat sink. However, correctly applying this principle requires carefully characterizing what actually constitutes "information erasure" within a given physical computing mechanism. In this paper, we show that abstract combinational logic networks can validly be considered to contain no information beyond that specified in their input, and that, because of this, appropriately-designed physical implementations of even multi-layer networks can in fact be updated in a single step while incurring no greater theoretical minimum energy dissipation than is required to update their inputs. Furthermore, this energy can approach zero if the network state is updated adiabatically via a reversible transition process. Our novel operational paradigm for updating logic networks suggests an entirely new class of hardware devices and circuits that can be used to reversibly implement Boolean logic with energy dissipation far below the Landauer limit.
For decades, neural networks have shown promise for next-generation computing, and recent breakthroughs in machine learning techniques, such as deep neural networks, have provided state-of-the-art solutions for inference problems. However, these networks require thousands of training processes and are poorly suited for the precise computations required in scientific or similar arenas. The emergence of dedicated spiking neuromorphic hardware creates a powerful computational paradigm which can be leveraged towards these exact scientific or otherwise objective computing tasks. We forego any learning process and instead construct the network graph by hand. In turn, the networks produce guaranteed success often with easily computable complexity. We demonstrate a number of algorithms exemplifying concepts central to spiking networks including spike timing and synaptic delay. We also discuss the application of cross-correlation particle image velocimetry and provide two spiking algorithms; one uses time-division multiplexing, and the other runs in constant time.
We address practical limits of energy efficiency scaling for logic and memory. Scaling of logic will end with unreliable operation, making computers probabilistic as a side effect. The errors can be corrected or tolerated, but overhead will increase with further scaling. We address the tradeoff between scaling and error correction that yields minimum energy per operation, finding new error correction methods with energy consumption limits about 2× below current approaches. The maximum energy efficiency for memory depends on several other factors. Adiabatic and reversible methods applied to logic have promise, but overheads have precluded practical use. However, the regular array structure of memory arrays tends to reduce overhead and makes adiabatic memory a viable option. This paper reports an adiabatic memory that has been tested at about 85× improvement over standard designs for energy efficiency. Combining these approaches could set energy efficiency expectations for processor-in-memory computing systems.
Bouchard, Kristofer E.; Aimone, James B.; Chun, Miyoung; T, Dean; Denker, Michael; Diesmann, Markus; Donofrio, David D.; Frank, Loren M.; Kasthuri, Narayanan; C, Koch; Ruebel, Oliver; Simon, Horst D.; Sommer, Friedrich T.; Prabhat, None
Opportunities offered by new neuro-technologies are threatened by lack of coherent plans to analyze, manage, and understand the data. High-performance computing will allow exploratory analysis of massive datasets stored in standardized formats, hosted in open repositories, and integrated with simulations.