A Variation-Aware Preferential Design Approach for Memory-Based Reconfigurable Computing

Static random access memory arrays designed in sub-90-nm technologies are highly vulnerable to process variation-induced read/write/access failures. In memory-based reconfigurable computing frameworks, which use large high-density memory array, such failures lead to incorrect execution of mapped applications. It causes loss in quality of service (QoS) for digital signal processing (DSP) applications. In this paper, we analyze the effect of parameter variations on QoS in a memory-based reconfigurable computing framework. Next, we propose a preferential design approach at both application mapping and circuit level, which can significantly improve QoS and yield under large parameter variations. The proposed application mapping process considers the reliability map of a memory array and maps the important components with respect to QoS to more reliable memory blocks under performance constraint. At circuit level, we exploit the read-dominant memory access pattern to skew the memory cells for better read stability leading to improved QoS. Such a architecture/circuit codesign approach can also tolerate increased failure rate at low operating voltage, thus facilitating low-power operation. The effect of the approach is studied for two common DSP applications, namely discrete cosine transform and finite-impulse response (FIR) filter. The simulation results for FIR application show 45% improvement in power at iso-QoS and 47% in yield for a target peak signal to noise ratio at 45-nm technology.