The Internet of Things (IoTs) has triggered rapid advances in sensors, surveillance devices, wearables and body area networks with advanced Human-Computer Interfaces (HCI). Neural Networks optimized algorithmically for high accuracy and high representation power are very deep and require tremendous storage and processing capabilities leading to higher area and power costs. For developing smart front-ends for ‘always on’ sensor nodes we need to optimize for power and area. This requires considering trade-offs with respect to various entities such as resource utilization, processing time, area, power, accuracy etc. Our experimental results show that there is presence of a network configuration with minimum energy given the input constraints of an application in consideration. This presents the need for a hardware-software co-design approach. We present a highly parameterized hardware design on an FPGA allowing re-configurability and the ability to evaluate different design choices in a short amount of time. We also describe the capability of extending our design to offer run time configurability. This allows the design to be altered for different applications based on need and also allows the design to be used as a cascaded classifier beneficial for continuous sensing for low power applications. This thesis aims to evaluate the use of Restricted Boltzmann Machines for building such re-configurable low power front ends. We develop the hardware architecture for such a system and provide experimental results obtained for the case study of Posture detection for body worn cameras used for law enforcement.