Computation kernels in emerging recognition, mining, and synthesis (RMS) applications are inherently error-resilient, where approximate computing can be applied to improve their energy efficiency by trading off computational effort and output quality. One promising approximate computing technique is to perform approximate computing with memory, which stores a subset of function responses in a lookup table (LUT), and avoids redundant computation when encountering similar input patterns. Limited by the memory space, most existing solutions simply store values for those frequently-appeared input patterns, without considering output quality and/or intrinsic characteristic of the target kernel. In this paper, we propose a novel LUT allocation technique for approximate computing with memory, which is able to dramatically improve the hit rate of LUT and hence achieves significant energy savings under given quality constraints. We also present how to apply the proposed LUT allocation solution for multiple computation kernels. Experimental results show the efficacy of our proposed methodology.

Many emerging applications are inherently error-resilient and hence do not require exact computation. Previous work on resilience-aware task allocation and scheduling problem first generates an initial energy-efficient task schedule on voltage-scalable multiprocessor system at design-time, and then conducts voltage adjustment according to runtime quality checking result. While energy efficiency improvements are quite encouraging, the final quality requirement might be violated because quality checkers are usually designed based on partial information and they are not always correct. In this paper, we propose to address the uncertainty issue of quality checkers in resilient task allocation and scheduling. To be specific, given the initial task schedule and quality checkers, we propose (i) a solution that ensures the final output quality with maximized probability, which can be applied to any approximate computing quality management systems; and (ii) a greedy runtime algorithm to achieve optimized energy efficiency gains. Experimental results on various task graphs demonstrate the efficacy of our proposed technique.

The most notorious faults for diagnosis in post-silicon validation are those that manifest themselves in a non-deterministic manner with system-level functional tests, where errors randomly appear from time to time even when applying the same workloads. In this work, we propose a novel diagnostic framework that resorts to dual-modular redundancy (DMR) for troubleshooting non-deterministic faults, namely RetroDMR. To be specific, we log the essential events (e.g., the sequence of thread migration) in the faulty run to record the mapping relationship between threads and their corresponding execution units. Then in the following diagnosis runs, we apply redundant multithreading (RMT) technique to reduce error detection latency, while at the same time we try to follow the thread migration sequence of the original run whenever possible. By doing so, RetroDMR significantly improves the reproduction rate and diagnosis resolution for non-deterministic faults, as demonstrated in our experimental results.

Approximate computing, being able to trade off computation quality and computational effort (e.g., energy) by exploiting the inherent error-resilience of emerging applications (e.g., recognition and mining), has garnered significant attention recently. No doubt to say, quality assurance is indispensable for satisfactory user experience with approximate computing, but this issue has remained largely unexplored in the literature. In this work, we propose a novel framework namely ApproxQA to tackle this problem, in which approximation mode tuning and rollback recovery are considered in a unified manner. To be specific, ApproxQA resorts to a two-level controller, in which the high-level approximation controller tunes approximation modes at a coarse-grained scale based on Q-learning while the low-level rollback controller judiciously determines whether to perform rollback recovery at a fine-grained scale based on the target quality requirement. Experimental results on various benchmark applications demonstrate that it significantly outperforms existing solutions in terms of energy efficiency with quality assurance.

Computing with memory, which stores function responses of some input patterns into lookup tables offline and retrieves their values when encountering similar patterns (instead of performing online calculation), is a promising energy-efficient computing technique. No doubt to say, with a given lookup table size, the efficiency of this technique depends on which function responses are stored and how they are organized. In this paper, we propose a novel adaptive approximate lookup table based accelerator, wherein we store function responses in a hierarchical manner with increasing fine-grained granularity and accuracy. In addition, the proposed accelerator provides lightweight compensation on output results at different precision levels according to input patterns and output quality requirements. Moreover, our accelerator conducts adaptive lookup table search by exploiting input locality. Experimental results on various computation kernels show significant energy savings of the proposed accelerator over prior solutions.

Many emerging applications are inherently error-resilient and hence do not require exact computation. In this paper, we consider the task allocation and scheduling problem for mapping such applications to voltage-scalable multiprocessor systems. The proposed solution, namely ApproxMap, judiciously determines the mapping and execution sequence of resilient tasks to minimize the energy consumption of the application while meeting their target quality requirements and timing constraints. To be specific, ApproxMap generates energy-efficient yet flexible task schedule at design-time, and conducts lightweight online adjustment according to runtime dynamics for further energy-efficiency improvement. Experimental results on various task graphs demonstrate the efficacy of ApproxMap.

Approximate computing, where computation quality is traded off for better performance and/or energy savings, has gained significant tractions from both academia and industry. With approximate computing, we expect to obtain acceptable results, but how do we make sure the quality of the final results are acceptable? This challenging problem remains largely unexplored. In this paper, we propose an effective and efficient quality management framework to achieve controlled quality-efficiency tradeoffs. To be specific, at the offline stage, our solution automatically selects an appropriate approximator configuration considering rollback recovery for large occasional errors with minimum cost under the target quality requirement. Then during the online execution, our framework judiciously determines when and how to rollback, which is achieved with cost-effective yet accurate quality predictors that synergistically combine the outputs of several basic light-weight predictors. Experimental results demonstrate that our proposed solution can achieve 11% to 23% energy savings compared to existing solutions under the target quality requirement.

Motivated by the inherent error-resilience of emerging recognition, mining, and synthesis (RMS) applications, approximate computing techniques such as precision scaling has been advocated for achieving energy-efficiency gains at the cost of small accuracy loss. Most existing solutions, however, focus on the approximation of on-chip computations without considering that of off-chip data accesses, whose energy consumption may contribute to a significant portion of the total energy. In this work, we propose a novel approximate memory access technique for dynamic precision scaling, namely ApproxMA. To be specific, by taking both runtime data precision constraints and error-resilient capabilities of the application into consideration, ApproxMA determines the precision of data accesses and loads scaled data from off-chip memory for computation. Experimental results with mixture model-based clustering algorithms demonstrate the efficacy of the proposed methodology.

Recognition, Mining, and Synthesis (RMS) applications are expected to make up much of the computing workloads of the future. Many of these applications (e.g., recommender systems and search engine) are formulated as finding eigenvalues/vectors of large-scale matrices. These applications are inherently error-tolerant, and it is often unnecessary, sometimes even impossible, to calculate all the eigenpairs. Motivated by the above, in this work, we propose a novel approximate computing technique for large-scale eigen-decomposition, namely ApproxEigen, wherein we focus on the practically-used Krylov subspace methods to find finite number of eigenpairs. With ApproxEigen, we provide a set of computation kernels with different levels of approximation for data pre-processing and solution finding, and conduct accuracy tuning under given quality constraints. Experimental results demonstrate that ApproxEigen is able to achieve significant energy-efficiency improvement while keeping high accuracy.

With aggressive CMOS technology scaling, Negative Bias Temperature Instability (NBTI) has emerged as one of the major system lifetime reliability threats, which gradually increases PMOS transistor threshold voltage and hence results in increased circuit delay. NBTI-induced performance degradation depends heavily on time-varying parameters such as temperature, duty cycle and supply voltage. Previous analytical models for NBTI effects, however, cannot cover all these parameters, causing overly optimistic or overly pessimistic analysis. In this work, we propose a comprehensive NBTI analytical model that explicitly takes supply voltage, duty cycle and temperature variations into consideration. The accuracy of the proposed model is validated against cycle-accurate simulation for NBTI effects. In addition, based on the proposed model, we present an efficient simulation framework for system lifetime prediction by running representative workloads only. Experimental results demonstrate the efficacy and efficiency of the proposed solution.

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.