With the continued scaling of chip manufacturing technologies, the significance of process variation in performance of the systems is increasing. Specifically, process variation results in growing voltage and frequency overhead margins required to ensure error free operation of circuits. However, the traditional practice of over-designing the systems to cover process variation is no longer an efficient design methodology in an age with high demands for processing power and limited energy supplies. In this dissertation, a novel analytical model is proposed to predict the required margin accurately in the early stages of design space exploration. The model can be used to optimize the system overhead in error free calculations or to release the bound by full correctness in error tolerant parts of systems and optimize the energy vs. performance trade-off. Additionally, this model also considers the statistics of the inputs of the circuit as compared to other existing efforts enabling it to achieve close predictions of full circuit simulation results in a short time. This model is finally used in an adaptive carry select/ripple carry adder configuration to demonstrate the potential achievable power savings.

Growing variation in newer technology nodes is not always a negative side effect. The increased inherent randomness in the process manufacturing technology can be utilized to develop unique physically unclonable functions (PUFs). These functions are irreproducible hardware-based authenticating systems, which do not require memory-based storage. A low overhead delay-based PUF using the variation of the silicon manufacturing is also proposed in the second part of this work. The proposed PUF uses a simple and efficient structure to convert the randomness of the manufacturing process into random responses to fixed challenges in identically designed circuits.