Energy harvesting technologies provide a promising alternative to battery-powered systems and create an opportunity to achieve sustainable computing for the exploitation of ambient energy sources. However, energy harv...Energy harvesting technologies provide a promising alternative to battery-powered systems and create an opportunity to achieve sustainable computing for the exploitation of ambient energy sources. However, energy harvesting devices and power generators encompass a number of non-classical system behaviors or characteristics, such as delivering nondeterministic power density, and these would create hindrance for effectively utilizing the harvested energy. Previously, we have investigated new design methods and tools that are used to enable power adaptive computing and, particularly, catering non-deterministic voltage, which can efficiently utilize ambient energy sources. Also, we developed a co-optimization approach to maximize the computational efficiency from the harvested ambient energy. This paper will provide a review of these methods. Emerging technologies, such as 3D-IC, which would also enable new paradigm of green and high-performance computing, will be also discussed.展开更多
In recent years,Approximate Computing Circuits(ACCs)have been widely used in applications with intrinsic tolerance to errors.With the increased availability of approximate computing circuit approaches,reliability anal...In recent years,Approximate Computing Circuits(ACCs)have been widely used in applications with intrinsic tolerance to errors.With the increased availability of approximate computing circuit approaches,reliability analysis methods for assessing their fault vulnerability have become highly necessary.In this study,two accurate reliability evaluation methods for approximate computing circuits are proposed.The reliability of approximate computing circuits is calculated on the basis of the iterative Probabilistic Transfer Matrix(PTM)model.During the calculation,the correlation coefficients are derived and combined to deal with the correlation problem caused by fanout reconvergence.The accuracy and scalability of the two methods are verified using three sets of approximate computing circuit instances and more circuits in Evo Approx8 b,which is an approximate computing circuit open source library.Experimental results show that relative to the Monte Carlo simulation,the two methods achieve average error rates of 0.46%and 1.29%and time overheads of 0.002%and 0.1%.Different from the existing approaches to reliability estimation for approximate computing circuits based on the original PTM model,the proposed methods reduce the space overheads by nearly 50%and achieve time overheads of 1.78%and 2.19%.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61176025 and No. 61006027
文摘Energy harvesting technologies provide a promising alternative to battery-powered systems and create an opportunity to achieve sustainable computing for the exploitation of ambient energy sources. However, energy harvesting devices and power generators encompass a number of non-classical system behaviors or characteristics, such as delivering nondeterministic power density, and these would create hindrance for effectively utilizing the harvested energy. Previously, we have investigated new design methods and tools that are used to enable power adaptive computing and, particularly, catering non-deterministic voltage, which can efficiently utilize ambient energy sources. Also, we developed a co-optimization approach to maximize the computational efficiency from the harvested ambient energy. This paper will provide a review of these methods. Emerging technologies, such as 3D-IC, which would also enable new paradigm of green and high-performance computing, will be also discussed.
基金supported by the National Natural Science Foundation of China(Nos.61432017 and 61772327)the Natural Science Foundation of Shanghai(Nos.20ZR1455900 and 20ZR1421600)+1 种基金the Qi'anxin National Engineering Laboratory for Big Data Collaborative Security Technology Open Project(No.QAX-201803)State Key Laboratory of Computer Architecture,Institute of Computing Technology,Chinese Academy of Sciences(No.CARCHA202005)。
文摘In recent years,Approximate Computing Circuits(ACCs)have been widely used in applications with intrinsic tolerance to errors.With the increased availability of approximate computing circuit approaches,reliability analysis methods for assessing their fault vulnerability have become highly necessary.In this study,two accurate reliability evaluation methods for approximate computing circuits are proposed.The reliability of approximate computing circuits is calculated on the basis of the iterative Probabilistic Transfer Matrix(PTM)model.During the calculation,the correlation coefficients are derived and combined to deal with the correlation problem caused by fanout reconvergence.The accuracy and scalability of the two methods are verified using three sets of approximate computing circuit instances and more circuits in Evo Approx8 b,which is an approximate computing circuit open source library.Experimental results show that relative to the Monte Carlo simulation,the two methods achieve average error rates of 0.46%and 1.29%and time overheads of 0.002%and 0.1%.Different from the existing approaches to reliability estimation for approximate computing circuits based on the original PTM model,the proposed methods reduce the space overheads by nearly 50%and achieve time overheads of 1.78%and 2.19%.
