Design and Analysis of Energy Efficient Approximate Multipliers for Image Processing and Deep Neural Network
Design and Analysis of Energy Efficient Approximate Multipliers for Image Processing and Deep Neural Network
Abstract:
As technology scales down, the critical charge (QC) of vulnerable nodes decreases, making SRAM cells more susceptible to soft errors in the aerospace industry. This article proposes a Soft-Error-Aware 16T (S8P8N) SRAM cell for aerospace applications to address this issue. The properties of S8P8N are evaluated and compared with 6T, DICE, QUCCE12T, WEQUATRO, RHBD10T, RHBD12T, S4P8N, SEA14T, and SRRD12T. Simulation results indicate that all vulnerable nodes and key node pairs of the proposed cell can recover to their original states when affected by a soft error. Additionally, it can recover from key multinode upsets. The write speed of the proposed cell is found to be reduced by 20.3%, 50.1%, 74.1%, 63.7%, and 50.41% compared to 6T, DICE, QUCCE12T, WEQUATRO, and RHBD10T, respectively. The read speed of the proposed cell is found to be reduced by 56.6%, 52.2%, 62.5%, and 35.2% compared to 6T, SRRD12T, RHBD12T, and S4P8N, respectively. It also shows that the hold power of the proposed cell is found to be reduced by 14.1%, 13.8%, 17.7%, and 23.4% compared to DICE, WEQUATRO, RHBD10T, and RHBD12T. Furthermore, the read static noise margin (RSNM) of the proposed cell is found to be enhanced by 157%, 67%, and 32% compared to RHBD12T, SEA14T, and SRRD12T. All these improvements are achieved with a slight area penalty.
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Design and Analysis of Energy Efficient Approximate Multipliers for Image Processing and Deep Neural Network
