Digital Multiplier is a fundamental component in many digital signal processing (DSP) systems, which takes up the most part of the computational resources. As many DSP applications have an inherent tolerance for inexact computations, approximate multiplication is considered as an appropriate substitution to obtain energy-performance-accuracy tradeoffs, especially in those applications that require high energy-efficiency in computing. Meanwhile, reducing the supply voltage is proved to be an efficient way to further lower the total energy consumption. In this paper, a novel approximate 4-2 compressor and its circuit implementation is proposed for error-resilient multiplication with a low supply voltage. Simulation results indicate that the approximate multiplier with our proposed approximate 4-2 compressor consumes the least energy per operation with the same computational accuracy when compared with other multipliers for the operand length of 8 bits. It achieves 26.7% reduction on energy-delay product (EDP) when compared with the exact multiplication.

This paper presents a novel 10T static random access memory (SRAM) cell for ultra-low voltage operations. A built-in read assist scheme is proposed to eliminate the read disturb issue. It significantly improves the read stability. In addition, a data-aware write assist technique is also proposed to enhance the write ability of the SRAM cell. Simulations are performed in a 40-nm standard CMOS technology at a 0.4V supply voltage. The experimental results indicate that the proposed 10T SRAM cell exhibits the best write margin and large read static noise margin. When compared with the conventional 6T SRAM cell, it achieves 2.95× larger read static noise margin and 1.93× higher write margin.

In recent years, approximate computing has been widely used in low power digital circuits design. As it incurs error in computation, there are always tradeoffs between hardware performances and computational accuracy. Thus, the circuit implementation can vary from applications to applications and even in the same application, which makes difficult to evaluate the design efficiency in terms of delay, power and computational accuracy. In this paper, approximate efficiency (AE) is proposed as a new design metric dedicated in energy-efficient approximate computation. An automatic pruning approach is presented based on quickly estimated AE, which leads to an efficient approximate circuit design and a significant reduction on energy dissipation. A 32-bit adder is demonstrated using proposed method and compared with other approximation methods, including direct truncation and significance-activity pruning method. For legitimate comparisons, same computational accuracy is obtained for all imprecise adders. The simulation results show that assessed on mean absolute error, the proposed adder outperforms the others. With given error threshold, it can reach 31.15% energy-delay-product reduction compared with the most competitive contender.

A line-feature-based approach for model based recognition using a four-dimensional Hausdorff distance is proposed. This approach reduces the problem of finding the rotation, scaling, and translation transformations between a model and an image to the problem of finding a single translation minimizing the Hausdorff distance between two sets of points in a four-dimensional space. The implementation of the proposed algorithm can be naturally extended to higher dimensional spaces to efficiently find correspondences between n-dimensional patterns. The method performance and sensitivity to segmentation problems are quantitatively characterized using an experimental protocol with simulated data. It is shown that the algorithm performs well, is robust to occlusion and outliers, and that it degrades nicely as the segmentation problems increase. Experiments with real images are also presented.

Two approaches for 3D curved object reconstruction using active sensor and illumination control are proposed and compared to each other. In both cases, the highlight information is fully utilized rather than discarded, and knowledge of the object surface is not required. The first approach requires camera control only and recovers shape (depth) from highlights and occluding contours. The second approach requires both camera and illumination control and recovers 3D depth from highlights only.

In this paper, a correlational approach for distinguishing occluding contours from object markings for 3D object modeling is presented. The proposed method is valid under weak perspective projection, does not require to search for correspondences between frames, can handle scaling between consecutive images. Thus can estimate the full Euclidean surface structure, and does not require camera calibration or camera motion measurement. Extensive experimental results show that the method is robust to the occlusion of feature points and image noise unlike previous affine-based approaches. Qualitative and quantitative results for the relation between the required minimum viewing angle change for the detection and the surface curvature are also presented.