• Energy Research
• 2021-2025close
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The rapidly growing number of edge devices continuously generating data with real-time response constraints coupled with the bandwidth, latency, and reliability issues of centralized cloud computing have made computing near the edge indispensable. As a result, using Field Programmable Gate Arrays (FPGAs) at the edge, due to their unique capabilities that meet the requirements of both high-performance applications and the Internet of Things (IoT) domain, is becoming prevalent. However, designs deployed on these devices suffer from efficiency gap versus custom implementations mainly due to the overhead associated with the FPGAs reconfigurability. This problem is more pronounced in the edge domain, where most devices are battery-powered. In the first part of this dissertation, we identify and overcome the challenges xvi behind the power reduction of FPGA-based applications and propose techniques to lower their energy consumption. Our approach exploits the pessimistic timing margin of the designs to tune the voltage and improves the energy consumption by 66%. An increasing number of edge applications rely on machine learning (ML) algorithms to generate useful insights from data. While modern machine learning techniques – in particular deep neural networks (DNNs) – can produce state-of-the-art results, they often entail substantial memory and compute requirements that may exceed the power and resources available on lightweight error-prone edge devices. Hyperdimensional Computing (HDC) is an emerging lightweight and robust learning paradigm suited for the edge domain that copes with the memory and compute overhead of conventional ML algorithms. The next part of the dissertation proposes efficient FPGA-based and custom hardware implementations of HDC to enable intelligence on devices with limited resources, strict energy constraints, and in noisy environments. The proposed HDC algorithms and accelerators reduce the energy consumption by more than three orders of magnitude compared to other ML solutions, with a ...

Teachers has a fundamental spot in improvement of training on the planet. Today situation in instruction area in agricultural nation like India where standard positions become old radically, individuals actually coming in this area as a result of their advantage in this area. A large portion of the new workforce chipping away at address premise or agreement premise and their positions are not gotten or might be gotten for a long time or scarcely any years relies on the agreement time frame. In the radiance of this foundation, the reason for this investigation is to survey and consider the association between uneasiness feeling with professional stability of agreement personnel working in designing universities of Punjab, India with the assistance of stress model to evaluate the fate of educating in emerging country. The examination has been led on employees working in private and government designing schools of rustic area of Gurdaspur locale of Punjab.