With the increasing adoption of Deep Neural Networks (DNNs) in modern applications, there has been a proliferation of computationally and power-hungry workloads, which has necessitated the use of embedded systems with more sophisticated, heterogeneous approaches to accommodate these requirements. One of the major solutions to tackle these challenges has been the development of domain-specific accelerators, which are highly optimized for the computationally intensive tasks associated with DNNs. These accelerators are designed to take advantage of the unique properties of DNNs, such as parallelism and data locality, to achieve high throughput and energy efficiency. Domain-specific accelerators have been shown to provide significant improvements in performance and energy efficiency compared to traditional general-purpose processors and are becoming increasingly popular in a range of applications such as computer vision and speech recognition. However, designing these architectures and managing their resources can be challenging, as it requires a deep understanding of the workload and the system's unique properties. Achieving a favorable balance between performance and power consumption is not always straightforward and requires careful design decisions to fully exploit the benefits of the underlying hardware. This dissertation aims to address these challenges by presenting solutions that enable low energy consumption without compromising performance for heterogeneous embedded systems. Specifically, this dissertation will focus on three topics: (i) the utilization of approximate computing concepts and approximate accelerators for energy-efficient DNN inference,(ii) the integration of formal properties in the systematic employment of approximate computing concepts, and (iii) resource management techniques on heterogeneous embedded systems.In summary, this dissertation provides a comprehensive study of solutions that can improve the energy efficiency of heterogeneous embedded systems, enabling them to perform computationally intensive tasks associated with modern applications that incorporate DNNs without compromising on performance. The results of this dissertation demonstrate the effectiveness of the proposed solutions and their potential for wide-ranging practical applications.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:dissertations-3125 |
| Date | 01 May 2023 |
| Creators | Spantidi, Ourania |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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