High-Efficiency Soft-Error-Tolerant Digital Signal Processing Using Fine-Grain Subword-Detection Processing

The soft error problem in digital circuits is becoming increasingly important as the IC fabrication technology progresses from the deep submicrometer scale to the nanometer scale. This paper proposes a subword-detection processing (SDP) technique and a fine-grain soft-error-tolerance (FGSET) architecture to improve the performance of the digital signal processing circuit. In the SDP technique, the logic masking property of the soft error in the combinational circuit is utilized to mask the single-event upset (SEU) caused by disturbing particles in the inactive area. To further improve the performance, the masked portion of the datapath can be used as the estimation redundancy in the algorithmic soft-error-tolerance (ASET) technique. This technique is called subword-detection and redundant processing (SDRP). In the FGSET architecture, the soft error in each processing element (fine grain) can be recovered by the arithmetic datapath-level ASET technique. Analysis of the fast Fourier transform processor example shows that the proposed FGSET architecture can improve the performance of the coarse-grain SET (CGSET) by 8.5 dB. The low-cost SDP technique (1.03$ times$) yields a noise reduction of 5.3 dB over the CGSET approach (1.40 $ times$), while the efficient SDRP I (1.57$ times$) and SDRP II (1.88$ times$ ) techniques outperform the CGSET approach by 24.5 and 30.5 dB, respectively.