Approximate Computing for Efficient Neural Network Computation: A Survey

The limitations of scaling in CMOS technology pose challenges in meeting the requirements of future applications. To address these challenges, researchers are exploring various design techniques, including Approximate Computing (AC), which leverages the inherent error resilience of applications to achieve high performance and energy gains with desired quality. AC has gained popularity as a computer paradigm for error-resilient applications, and many researchers have studied AC across computing layers and developed tools for implementing these techniques. This paper provides a comprehensive survey of AC techniques at the abstraction levels of software and hardware and discusses the tools to implement AC in hardware and software, quality evaluation tools and comparison points. The paper also covers existing frameworks for AC, potential applications, future research directions, challenges and limitations. This information can guide researchers in identifying promising avenues for further advancements and innovations in this domain. Additionally, this paper compares state-of-the-art surveys of AC and highlights the unique features and contributions of this work that distinguish our work from previous surveys.

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Approximate Computing for Efficient Neural Network Computation: A Survey

Cited by 2 publications

References 61 publications

Efficient Approximate Posit Multipliers for Deep Learning Computation

Efficient Approximate Posit Multipliers for Deep Learning Computation

Approximate Computing: Hardware and Software Techniques, Tools and Their Applications

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