IEEE Transactions on Circuits and Systems II: Express Briefs | 2019
Energy Efficient Single-Ended 6-T SRAM for Multimedia Applications
Abstract
Image processing and other multimedia applications require large embedded storage. In some of the earlier works approximate memory has been shown as a potential energy-efficient solution for such error-tolerant applications. In this brief, we propose a single ended 6-T (SE6T) static random access memory (SRAM) cell which has about 50% less dynamic power compared to conventional 6-T SRAM cell with the same bit error rate (BER). Since image processing applications are tolerant to errors, ultra low voltage power-efficient embedded memories with BER can be used for storage. We show that 1 KB (<inline-formula> <tex-math notation= LaTeX >$256 \\times 32$ </tex-math></inline-formula>) SE6T memory consumes <inline-formula> <tex-math notation= LaTeX >$ {0.45\\times }$ </tex-math></inline-formula> dynamic power, <inline-formula> <tex-math notation= LaTeX >$ {0.83\\times }$ </tex-math></inline-formula> leakage power and takes <inline-formula> <tex-math notation= LaTeX >$ {0.60\\times }$ </tex-math></inline-formula> area as compared to conventional 6T SRAM memory for similar peak signal to noise ratio (PSNR). We have also proposed heterogeneous SE6T 1K SRAM memory and show that for a given power budget, PSNR enhances by at least by 14 dB compared to when homogeneous (identically sized bit-cells) SE6T SRAM memory are used. When compared with heterogeneous 6T SRAM memory, the heterogeneous SE6T SRAM memory consumes <inline-formula> <tex-math notation= LaTeX >$ {0.44\\times }$ </tex-math></inline-formula> dynamic power, <inline-formula> <tex-math notation= LaTeX >$ {0.86\\times }$ </tex-math></inline-formula> leakage power and takes <inline-formula> <tex-math notation= LaTeX >$ {0.6\\times }$ </tex-math></inline-formula> area for almost similar PSNR. For a given PSNR, the SE6T memory is <italic>cumulatively</italic> better in terms of design complexity, area and power when compared with other hybrid and heterogeneous approximate memories.