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In this paper, we apply the voltage overscaling (VOS) technique to the residue-number-system (RNS)-based digital signal processing system for achieving high energy efficiency. To mitigate the soft errors caused by VOS, we propose a new method, called joint RNS-RPR (JRR), which is the combination of RNS and the reduced precision redundancy (RPR) technique. The JRR technology inherits the properties of RNS, including shorter critical path, low complexity, and low power. Moreover, JRR can achieve higher power reduction than RNS for VOS applications. Since the soft errors caused by VOS lead to significant performance degradation of RNS, we use the information from RNS and RPR to achieve a high recovering probability of the soft errors with low hardware complexity. From the case study of finite impulse response (FIR) filter design based on the 0.25-\mum 2.5-V CMOS technology, we find that JRR can save 62% more energy compared to the traditional FIR with a less than 2-dB signal noise ratio performance loss. We also find that JRR has lower complexity and better performance than the traditional soft error mitigation methods. [ABSTRACT FROM PUBLISHER]

We propose an effective scheme based on redundant residue number systems (RRNS) for radiation hardening of datapath in this paper. The proposed hardening scheme employs redundant residues to mitigate soft errors. We use the properties of RRNS, such as independence, parallel and error correction, to establish the radiation hardening architecture for datapath in radiation environments. In the architecture, all of the residues can be processed independently, and most of soft errors in datapath can be corrected with the redundant relationship of the residues at correction module, which is allocated at the end of the architecture. There is not any additional processing in the architecture but correction operation and binary to residue translation. Thus, the proposed scheme has high efficiency when the processing steps of datapath are large. In order to provide protection for the key module in the proposed scheme, correction module, some traditional protection methods, including Guard-gate register triple modular redundancy (G-RTMR), pipeline-level TMR, and module-level TMR, are used to construct the hybrid protection schemes. In the case studies, we find that the RRNS based scheme and the hybrid schemes can reduce soft error rate from 10-12 to 10-17, -18,10-25, and -25-respectively, only with 45.-46.1% area overheads and negligible latency overheads, when the pipeline stages of datapath are 104 and the moduli set with correction capability of one residue is adopted. The case studies also show that the proposed schemes can achieve less area and latency overheads than that of datapath without radiation hardening, because RRNS can reduce the complexity of operations in datapath. [ABSTRACT FROM AUTHOR]

Digital filter is an important fundamental component in digital signal processing (DSP) systems. Among the digital filters, the finite impulse response (FIR) filter is one of the most commonly used schemes. As a low-complexity hardware implementation technique, stochastic computing has been applied to overcome the huge hardware cost problem of high-order FIR filters. However, the stochastic FIR filter (SFIR) scheme suffers from long processing latency and accuracy degradation. In this paper, the bit stream representation noise is theoretically analyzed, and an adaptive scaling algorithm (ASA) is proposed to improve the accuracy of SFIR with the same bit stream length. Furthermore, a novel antithetic variables method is proposed to further improve the accuracy. According to the simulation results on a 64-tap FIR filter, the ASA and AV methods gain 17 dB and 6 dB on the signal-to-noise ratio (SNR), respectively. The hardware implementation results are also presented in this paper, which illustrates that the proposed ASA-AV-SFIR filter increases 4.6 times hardware efficiency with respect to the existing SFIR schemes. [ABSTRACT FROM AUTHOR]

The Residue Number System (RNS) is a non-weighted number system. Benefiting from its inherent parallelism, RNS has been widely studied and used in Digital Signal Processing (DSP) systems and cryptography. However, since the dynamic range in RNS has been fixed by its moduli set, it is hard to solve the overflow problem, which can be easily solved in Two's Complement System (TCS) by expanding the bit-width of it. For the multiplication in RNS, the traditional way to deal with overflow is to scale down the inputs so that the result can fall in its dynamic range. However, it leads to a loss of precision. In this paper, we propose a high-precision RNS multiplier for three-moduli set 2 n − 1 , 2 n , 2 n + 1 , which is the most used moduli set. The proposed multiplier effectively improves the calculation precision by adding several compensatory items to the result. The compensatory items can be obtained directly from preceding scalers with little extra effort. To the best of our knowledge, we are the first one to propose a high-precision RNS multiplier for the moduli set 2 n − 1 , 2 n , 2 n + 1 . Simulation results show that the proposed RNS multiplier can get almost the same calculation precision as the TCS multiplier with respect to Mean Square Error (MSE) and Signal-to-Noise Ratio(SNR), which outperforms the basic scaling RNS multiplier about 2.6–3 times with respect to SNR. [ABSTRACT FROM AUTHOR]