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 comparable or better accuracy. The last part of the dissertation seeks to resolve the privacy concerns of HDC that stem from its reversible algorithm and pose challenges for HDC-based learning and inference. We propose hardware- and communication-efficient techniques that improve the ‘inference’ privacy of HDC by reducing the information of the transferred data while consuming less energy than the non-private baseline. We then show that HDC ‘learning’ can meet tight privacy budgets with negligible accuracy degradation. We also propose a hybrid CNN and HDC model for differentially-private training over image data, which achieves comparable or better accuracy than the state-of-the-art CNN-only methods with more than three orders of magnitude faster training.

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