Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications
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arXiv
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| Format: | Preprint |
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2023
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| author | Leon, Vasileios Hanif, Muhammad Abdullah Armeniakos, Giorgos Jiao, Xun Shafique, Muhammad Pekmestzi, Kiamal Soudris, Dimitrios |
| author_facet | Leon, Vasileios Hanif, Muhammad Abdullah Armeniakos, Giorgos Jiao, Xun Shafique, Muhammad Pekmestzi, Kiamal Soudris, Dimitrios |
| contents | The challenging deployment of compute-intensive applications from domains such as Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches. Approximate Computing appears as an emerging solution, allowing to tune the quality of results in the design of a system in order to improve the energy efficiency and/or performance. This radical paradigm shift has attracted interest from both academia and industry, resulting in significant research on approximation techniques and methodologies at different design layers (from system down to integrated circuits). Motivated by the wide appeal of Approximate Computing over the last 10 years, we conduct a two-part survey to cover key aspects (e.g., terminology and applications) and review the state-of-the art approximation techniques from all layers of the traditional computing stack. Part II of the survey classifies and presents the technical details of application-specific and architectural approximation techniques, which both target the design of resource-efficient processors/accelerators and systems. Moreover, it reports a quantitative analysis of the techniques and a detailed analysis of the application spectrum of Approximate Computing, and finally, it discusses open challenges and future directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_11128 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications Leon, Vasileios Hanif, Muhammad Abdullah Armeniakos, Giorgos Jiao, Xun Shafique, Muhammad Pekmestzi, Kiamal Soudris, Dimitrios Hardware Architecture Artificial Intelligence Emerging Technologies Programming Languages The challenging deployment of compute-intensive applications from domains such as Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches. Approximate Computing appears as an emerging solution, allowing to tune the quality of results in the design of a system in order to improve the energy efficiency and/or performance. This radical paradigm shift has attracted interest from both academia and industry, resulting in significant research on approximation techniques and methodologies at different design layers (from system down to integrated circuits). Motivated by the wide appeal of Approximate Computing over the last 10 years, we conduct a two-part survey to cover key aspects (e.g., terminology and applications) and review the state-of-the art approximation techniques from all layers of the traditional computing stack. Part II of the survey classifies and presents the technical details of application-specific and architectural approximation techniques, which both target the design of resource-efficient processors/accelerators and systems. Moreover, it reports a quantitative analysis of the techniques and a detailed analysis of the application spectrum of Approximate Computing, and finally, it discusses open challenges and future directions. |
| title | Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications |
| topic | Hardware Architecture Artificial Intelligence Emerging Technologies Programming Languages |
| url | https://arxiv.org/abs/2307.11128 |