Hierarchical power distribution with a power tree has been developed. The key features are a power-tree structure with three power-tree management rules and a distributed common power domain implementation. The hierarchical power distribution supports a fine-grained power gating with dozens of power domains, which is analogous to a fine-grained clock gating. Leakage currents of a 1 000 000-gate power domain were effectively reduced to 1/4000 in multi-CPU SoCs with minimal area overhead
Self-report measures (e.g., Likert scales) are widely used to evaluate subjective health perceptions. Recently, the visual analog scale (VAS), a slider-based scale, has become popular owing to its ability to precisely and easily assess how people feel. These data can be influenced by the response style (RS), a user-dependent systematic tendency that occurs regardless of questionnaire instructions. Despite its importance, especially in between-individual analysis, little attention has been paid to handling the RS in the VAS (denoted as response profile (RP)), as it is mainly used for within-individual monitoring and is less affected by RP. However, VAS measurements often require repeated self-reports of the same questionnaire items, making it difficult to apply conventional methods on a Likert scale. In this study, we developed a novel RP characterization method for various types of repeatedly measured VAS data. This approach involves the modeling of RP as distributional parameters ${\theta}$ through a mixture of RS-like distributions, and addressing the issue of unbalanced data through bootstrap sampling for treating repeated measures. We assessed the effectiveness of the proposed method using simulated pseudo-data and an actual dataset from an empirical study. The assessment of parameter recovery showed that our method accurately estimated the RP parameter ${\theta}$, demonstrating its robustness. Moreover, applying our method to an actual VAS dataset revealed the presence of individual RP heterogeneity, even in repeated VAS measurements, similar to the findings of the Likert scale. Our proposed method enables RP heterogeneity-aware VAS data analysis, similar to Likert-scale data analysis.
In the near future, the performance growth of Neumann-architecture computers will slow down due to the end of semiconductor scaling. Presently a new computing paradigm, so-called natural computing, which maps problems to physical models and solves the problem by its own convergence property, is expected. The analog computer using superconductivity from D-Wave [1] is one of those computers. A neuron chip [2] is also one of them. We proposed a CMOS-type Ising computer [3]. The Ising computer maps problems to an Ising model, a model to express the behavior of magnetic spins (the upper left diagram in Fig. 24.3.1), and solves the problems by ground-state search operations. The energy of the system is expressed by the formula in the diagram. Computing flows are expressed in the lower flow chart in Fig. 24.3.1. In the conventional Neumann architecture, the problem is sequentially and repeatedly calculated, and therefore, the number of computing steps drastically increases as the problem size grows. In the Ising computer, in the first step, the problem is mapped to the Ising model. In the next steps, an annealing operation, the ground-state search by interactions between spins, are activated and the state transitions to the ground state where the energy of the system is minimized. The interacting operation between spins is decided by the interaction coefficients, which are set to each connection. Here, the configuration of the interaction coefficients is decided by the problem, and therefore, the interaction coefficients are equivalent to the programming in the conventional computing paradigm. The ground state corresponds to the solution of the original problem, and the solution is acquired by observing the ground state. The interactions for the annealing are performed in parallel, and the necessary steps for the annealing are smaller than that used by a sequential computing, Neumann architecture. As the table in Fig. 24.3.1, our Ising computer uses CMOS circuits to express the Ising model, and acquires the scalability and operation at room temperature.
Hierarchical power distribution with a power tree has been developed. The key features are power tree management rules and a distributed common power-domain implementation. The hierarchical power distribution supports a fine-grained power gating with dozens of power domains, which is analogous to a fine-grained clock gating. Leakage currents of a 1,000,000-gate power domain were effectively reduced to 1/4,000 in multi-CPU SoCs with minimal area overhead.
Abstract It is necessary to load single electrons into individual quantum dots (QDs) in an array for implementing fully scalable silicon-based quantum computers. However, this single-electron loading would be impacted by the variability of the QD characteristics, and suppressing this variability is highly challenging even in the state-of-the-art silicon front-end process. Here, we used a single-electron pump (SEP) for loading single electrons into a QD array as a preparatory step to use electrons as spin qubits. We used parallel gates in the QD array as a SEP and demonstrated 100 MHz operation with an accuracy of 99% at 4 K. By controlling the timing of a subsequent gate synchronously as a shutter, we found that the jitter representing electron transfer was less than 10 ns, which would be acceptable for a typical operating speed of around 1 MHz for silicon qubits.
Flame spread phenomena in a suspended fuel droplet array were experimentally investigated for n-hexadecane in a high-pressure ambience. Seven droplets of the same size were arranged at equal horizontal spacings. Flame spread rates were measured based on OH emission histories detected by a high-speed video camera with an image intensifier for droplet diameters of 0.50, 0.75, and 1.0 mm at ambient pressures from 0.1 to 2.0 MPa. Results show that, as droplet spacing becomes smaller, flame spread rate increases and attains a maximum value at a specific spacing. A further decrease in droplet spacing causes the spread rate to decrease due to the large latent heat of vaporization. Experiments were also conducted in a microgravity field to determine if these characteristics of flame spread are affected by natural convection.
