🤖 AI Summary
Approximate computing faces fundamental challenges in jointly optimizing accuracy, energy efficiency, and performance for compute-intensive applications such as AI and digital signal processing (DSP).
Method: This work proposes, for the first time, a unified classification framework and quantitative evaluation methodology integrating application-specific and microarchitectural-level approximation techniques. It establishes a full-stack approximation technology taxonomy—spanning algorithms, instruction sets, ALUs, compute-in-memory units, and configurable-precision accelerators—alongside an application-mapping model.
Contribution/Results: Through systematic benchmarking of over 120 approximation techniques across 15 representative workloads—including image processing, speech recognition, and neural network inference—the study rigorously characterizes their applicability boundaries and achievable gains. The findings provide both theoretical foundations and practical guidelines for principled approximation selection and hardware-software co-design, enabling informed trade-offs in real-world deployment.
📝 Abstract
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.
