With the growing complexity of modern micro-architectures, processor design must accommodate a wide array of parameters, resulting in vast design spaces. Identifying the optimal trade-offs among various design metrics poses a significant challenge. Performance is inherently difficult to model analytically, while area can be represented by analytical models. Performance evaluation relies on expensive and time-consuming cycle-accurate simulators (CAS), which puts forward strict requirements on the convergence speed and data dependence of the optimization methods. In this paper, based on the characteristics of the design metrics, the processor design problem is modeled as a hybrid black-box and white-box multi-objective discrete optimization problem (BWMO-DOP). In engineering applications, parallel simulation is a common acceleration technology. Therefore, white-box objective, area, is formulated as parallel constraints, while black-box objective, performance, is approximated by order-preserving surrogate objective. And then, BWMO-DOP is simplified into parallel single-objective expensive black-box optimization problems, which are solved by an efficient SOP-MOOA. SOP-MOOA iteratively explores more design points, enhancing the accuracy of the surrogate model while simultaneously updating the Pareto set. Experimental results demonstrate that the proposed algorithm outperforms baseline algorithm in an engineering case and three general numerical cases. In the engineering experiment for performance-area optimization, the proposed algorithm outperforms the baseline algorithm by a factor of more than two when considering the combined effect of performance improvement percentage and area reduction percentage. In numerical tests conducted on three 40-dimensional black-box functions under the same evaluation overhead, the proposed algorithm consistently identified Pareto set of superior quality.

Design space exploration (DSE), which aims to optimize the parameter configurations for the processor with a limited evaluation budget, is an important but challenging problem. Processor performance for a given parameter configuration can only be obtained through expensive evaluations. A key characteristic of processor DSE is that the optimization objective is the benchmark score, which is typically computed as a weighted combination of multiple sub-benchmark scores. Each sub-benchmark score can only be obtained through a costly evaluation. Bayesian optimization (BO) is widely used for DSE through an iterative process, where a surrogate model is constructed and updated to guide the selection of parameter configurations in each iteration. However, during the optimization process, it is not always necessary to evaluate all subbenchmarks for every recommended parameter configuration. In some cases, evaluating only a subset of sub-benchmarks can still provide significant improvements to the surrogate model. This paper proposes a BO-based algorithm that effectively utilizes the evaluation budget for efficient optimization. In each iteration, the evaluation budget is divided into two parts: exploitation and exploration. For the exploitation part, we propose a diversity-aware horse racing selection rule and use ordinal optimization to identify configurations with a high likelihood of being optimal. They are evaluated on all sub-benchmarks to obtain the total scores. For the exploration part, we propose a conditional entropy-based greedy algorithm to allocate the evaluation budget across parameter configurations and their corresponding sub-benchmarks, which can effectively improve the surrogate model. We conduct a comprehensive numerical experiment to validate the effectiveness of both components of our algorithm. Furthermore, experiments on three real-world DSE problems demonstrate that our algorithm outperforms state-of-the-art Bayesian optimization methods.

Design Space Exploration (DSE) in processors is an expensive discrete simulation optimization problem. The data requirements of the regular data-driven methods are so large that it is challenging to converge to a satisfactory solution within a limited simulation budget. Based on binary integer linear programming (BILP), an iteratively piecewise linear approximate method (IPLAM) is proposed for this kind of problems to reduce the dependence of simulation data. IPLAM starts from an initial reference point. Each iteration generates a set of trial points based on the most promising reference point by piecewise shift method. After evaluating the trial points, a local surrogate model is constructed for the unit neighborhood of the reference point. The surrogate model is then used to guide the exploration of the next reference point. In theory, IPLAM can converge to the global optimal point under mild assumptions, which is further verified by the numerical experiments. Meanwhile, the numerical experiments demonstrate that IPLAM outperforms the advanced Bayesian optimization and differential evolution methods on high-dimensional discrete black-box test functions. Besides, the practical effectiveness of IPLAM is validated by an industrial case for processor DSE.

One of the major sources of conservativeness in min-max model predictive control (MPC) is the handling of constraints, where the ellipsoidal robust invariant set is utilized for the proposal of sufficient and conservative conditions for the satisfaction of the constraints. In this article, in order to reduce the conservativeness due to the constraint handling, we add additional relaxation variables to the physical constraints and propose a two-step approach through relaxing the constraints by the amount which is determined by the calculating of the maximal admissible set (MAS). The constraints are relaxed in an iterative manner to avoid the constraints violation, and the constraint relaxation variables are degrees of freedom for relaxing the constraints and improving the control performance. Moreover, we show that under certain circumstance, the physical constraints can be removed without the constraint violation. The proposed approach is shown to be recursively feasibility and its effectiveness is verified through an air conditioning control in a building energy system. Note to Practitioners—Buildings are account for large percentage of worldwide energy consumptions. One of the most applicable method for optimization of building energy system subject to multiple constraints is the model predictive control (MPC). However, the industrial MPC is usually not recursively feasible, which implies that the optimization problem can become infeasible and the software will be terminated at some time. In order to apply the MPC synthesis approach (MPC with recursive feasibility guarantee), we have to overcome the conservativeness problem due to the handling of constraints. We propose a useful approach in this work by introducing relaxation variables which act as degrees of freedom for improving the control performance, while the physical constraints are still satisfied. The proposed approach is verified through an example of a 24m2 office room located in Cyber-Physical Energy System (CPES) lab in Western China Science and Technology Innovation Harbour in Xianyang, China. The numerical results show the performance improve of the proposed approach.

The minimum band reflectance based compositing method is a main compositing method for retrieval of land surface reflectance from multi-temporal observations. But the shadow effect is difficult to be removed. In this paper, a new method that can identify and remove the shadow pixels from multi-temporal data is proposed. It compares the least two pixels to determine the possibility of the first pixel as shadow. If the first pixel is shadow, it is discarded. The shadow pixels can he removed from the multi-temporal observations after iteration of this procedure two or three times. It has been tested with MODIS data and the results showed it could operate effectively over different land cover types.

The burned area is an important parameters for modelling the carbon cycles. The remote sensing tenology is a only way to monitor it at large scale region. In general, two temporal vegetation index difference was used to detect the burned area. But this technology is difficult to be applied to large-scale region owing to the BRDF effect, atmospheric contamination, geolocation errors, phenological changes, vegetation regrowth and others. In this paper, a new approach was proposed to detect the burned area using MODIS data, which is based on the vector-change technology, and combines the MODIS 500 and 250 meter resolution bands data to find 250 meter resolution burned area. The method adequately uses the spectral and multi-spatial resolution character of MODIS data that can resist the noise pollution and improve the detection accuracy. The detection results are very corresponding with the visual interpretation under different background. Since the method needs no prior knowledge, it could also be applied in large region scale. Based on this algorithm, the burned area dataset covering all China from 2000 to 2004 were produced. © 2005 IEEE.

A change vector based method for extracting flooded area from multi-temporal ENVISAT ASAR image is presented in this paper. The high resolution and multi-polarisation modes of ENVISAT ASAR make itself a useful tool for flood monitoring. The vector change method, which has been used extensively to detect the change of land surface in optical remote sensing, can take advantage of the reference images information recorded during, the non-flooded period so it is more robust than other change detection approaches. This technology has been applied to monitor flood in Dongting Lake, China using six temporal ASAR data from June to October 2004.