@inproceedings{23608c6decde466fb0958f0d02ff8104,
title = "CANN: Curable approximations for high-performance deep neural network accelerators",
abstract = "Approximate Computing (AC) has emerged as a means for improving the performance, area and power-/energy-efficiency of a digital design at the cost of output quality degradation. Applications like machine learning (e.g., using DNNs-deep neural networks) are highly computationally intensive and, therefore, can significantly benefit from AC and specialized accelerators. However, the accuracy loss introduced because of approximations in the DNN accelerator hardware can result in undesirable results. This paper presents a novel method to design high-performance DNN accelerators where approximation error(s) from one stage/part of the design is {"}completely{"} compensated in the subsequent stage/part while offering significant efficiency gains. Towards this, the paper also presents a case-study for improving the performance of systolic array-based hardware architectures, which are commonly used for accelerating state-of-the-art deep learning algorithms.",
keywords = "Accelerator, Approximate Computing, DNN, Energy Efficiency, High-Performance, MAC, Neural Network, Power Efficiency",
author = "Hanif, {Muhammad Abdullah} and Faiq Khalid and Muhammad Shafique",
note = "Funding Information: This research was supported by the Marsden Fund, administered by the Royal Society of New Zealand, and the University of Otago. Discussions with P.O. Koons, R. McDowall, and R.J. Norris helped to develop ideas, and i.M. Turnbull and P.J. Forsyth were helpful in sharing ideas on landscape evolution. Constructive reviews by R. McDowall and a.S. Jayko helped to improve the presentation and substance of the manuscript. Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.; 56th Annual Design Automation Conference, DAC 2019 ; Conference date: 02-06-2019 Through 06-06-2019",
year = "2019",
month = jun,
day = "2",
doi = "10.1145/3316781.3317787",
language = "English (US)",
series = "Proceedings - Design Automation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019",
}