@inproceedings{eb043c3485ee4b2aa689b80126ed0165,
title = "Approximate Computing for ML: State-of-the-art, Challenges and Visions",
abstract = "In this paper, we present our state-of-the-art approximate techniques that cover the main pillars of approximate computing research. Our analysis considers both static and reconfigurable approximation techniques as well as operation-specific approximate components (e.g., multipliers) and generalized approximate highlevel synthesis approaches. As our application target, we discuss the improvements that such techniques bring on machine learning and neural networks. In addition to the conventionally analyzed performance and energy gains, we also evaluate the improvements that approximate computing brings in the operating temperature.",
keywords = "Accelerator, Approximate Computing, Architecture, High-Level Synthesis, Inference, Logic, Low-power, Multiplier, Neural Network, Renconfigurable Accuracy, Temperature",
author = "Georgios Zervakis and Hassaan Saadat and Hussam Mrouch and Andreas Gerstlauer and Sri Parameswaran and Jorg Henkel",
note = "Publisher Copyright: {\textcopyright} 2021 Association for Computing Machinery.; 26th Asia and South Pacific Design Automation Conference, ASP-DAC 2021 ; Conference date: 18-01-2021 Through 21-01-2021",
year = "2021",
month = jan,
day = "18",
doi = "10.1145/3394885.3431632",
language = "English",
series = "Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "189--196",
booktitle = "Proceedings of the 26th Asia and South Pacific Design Automation Conference, ASP-DAC 2021",
}