Weija Zhang

Bio: Weija Zhang is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Signal processing & Very-large-scale integration. The author has an hindex of 1, co-authored 1 publications receiving 244 citations.

Design of Low-Power High-Speed Truncation-Error-Tolerant Adder and Its Application in Digital Signal Processing

Abstract: In modern VLSI technology, the occurrence of all kinds of errors has become inevitable. By adopting an emerging concept in VLSI design and test, error tolerance (ET), a novel error-tolerant adder (ETA) is proposed. The ETA is able to ease the strict restriction on accuracy, and at the same time achieve tremendous improvements in both the power consumption and speed performance. When compared to its conventional counterparts, the proposed ETA is able to attain more than 65% improvement in the Power-Delay Product (PDP). One important potential application of the proposed ETA is in digital signal processing systems that can tolerate certain amount of errors.

Approximate computing: An emerging paradigm for energy-efficient design

Abstract: Approximate computing has recently emerged as a promising approach to energy-efficient design of digital systems. Approximate computing relies on the ability of many systems and applications to tolerate some loss of quality or optimality in the computed result. By relaxing the need for fully precise or completely deterministic operations, approximate computing techniques allow substantially improved energy efficiency. This paper reviews recent progress in the area, including design of approximate arithmetic blocks, pertinent error and quality measures, and algorithm-level techniques for approximate computing.

Accuracy-configurable adder for approximate arithmetic designs

Abstract: Approximation can increase performance or reduce power consumption with a simplified or inaccurate circuit in application contexts where strict requirements are relaxed. For applications related to human senses, approximate arithmetic can be used to generate sufficient results rather than absolutely accurate results. Approximate design exploits a tradeoff of accuracy in computation versus performance and power. However, required accuracy varies according to applications, and 100% accurate results are still required in some situations. In this paper, we propose an accuracy-configurable approximate (ACA) adder for which the accuracy of results is configurable during runtime. Because of its configurability, the ACA adder can adaptively operate in both approximate (inaccurate) mode and accurate mode. The proposed adder can achieve significant throughput improvement and total power reduction over conventional adder designs. It can be used in accuracy-configurable applications, and improves the achievable tradeoff between performance/power and quality. The ACA adder achieves approximately 30% power reduction versus the conventional pipelined adder at the relaxed accuracy requirement.

Enhanced low-power high-speed adder for error-tolerant application

Abstract: The tradeoff between power consumption and speed performance has become a major design consideration when devices approach the sub-100 nm regime. It is especially critical when dealing with large data set, whereby the system is degraded in terms of power and speed. If the application can accept some errors, i.e. the application is Error — tolerant (ET), a large reduction in power and an increased in speed can be simultaneously achieved. In this paper, we shall present a novel low-power and high-speed Error-Tolerant Adder Type IV design called ETAIV. The proposed ETAIV is an enhancement of our earlier design, ETAII [1] in terms of speed and accuracy.

MACACO: modeling and analysis of circuits for approximate computing

Abstract: Approximate computing, which refers to a class of techniques that relax the requirement of exact equivalence between the specification and implementation of a computing system, has attracted significant interest in recent years. We propose a systematic methodology, called MACACO, for the Modeling and Analysis of Circuits for Approximate Computing. The proposed methodology can be utilized to analyze how an approximate circuit behaves with reference to a conventional correct implementation, by computing metrics such as worst-case error, average-case error, error probability, and error distribution. The methodology applies to both timing-induced approximations such as voltage over-scaling or over-clocking, and functional approximations based on logic complexity reduction. The first step in MACACO is the construction of an equivalent untimed circuit that represents the behavior of the approximate circuit at a given voltage and clock period. Next, we construct a virtual error circuit that represents the error in the approximate circuit's output for any given input or input sequence. Finally, we apply conventional Boolean analysis techniques (SAT solvers, BDDs) and statistical techniques (Monte-Carlo simulation) in order to compute the various metrics of interest. We have applied the proposed methodology to analyze a range of approximate designs for datapath building blocks. Our results show that MACACO can help a designer to systematically evaluate the impact of approximate circuits, and to choose between different approximate implementations, thereby facilitating the adoption of such circuits for approximate computing.

On reconfiguration-oriented approximate adder design and its application

Abstract: Approximate circuit designs allow us to tradeoff computation quality (e.g., accuracy) and computational effort (e.g., energy), by exploiting the inherent error-resilience of many applications. As the computation quality requirement of an application generally varies at runtime, it is preferable to be able to reconfigure approximate circuits to satisfy such needs and save unnecessary computational effort. In this paper, we present a reconfiguration-oriented design methodology for approximate circuits, and propose a reconfigurable approximate adder design that degrades computation quality gracefully. The proposed design methodology enables us to achieve better quality-effort tradeoff when compared to existing techniques, as demonstrated in the application of DCT computing.