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VLSI systems in the nanometer regime suffer from high defect rates and large parametric variations that lead to yield loss as well as reduced reliability of operation. An architectural framework that ensures proper system operation when few functional units are defective or unreliable under process-induced or temporal parametric variations can be effective in improving manufacturing yield and overall system reliability. In this paper, we propose a novel memory-based computational framework that exploits the on-chip memory to perform computation on demand using a lookup table (LUT)-based approach. The framework achieves reliable operation by transferring activity to embedded memory of a processor from a defective or unreliable functional unit. This allows the die to run at a reduced (but acceptable) performance level instead of being completely discarded due to unit failure (in case of defective functional unit) or being throttled (in case of temporal parameter variations, e.g., temperature induced variations). We note that although the worst-case latency of memory based computation can be considerably higher than regular operation latency, the average latency is only modestly higher due to the abundance of narrow-width operands. Furthermore, the operands for a specific instruction (e.g., integer add, multiply, or floating point add) experience high locality of reference and thus require loading only part of the LUTs in the cache. Simulation results for a set of benchmark applications show that the proposed scheme can significantly improve yield and reliability at the cost of only a small loss in performance (on an average 0.8%) and 10 × less area overhead compared to hardware duplication based defect tolerance approach.