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Learning Automata based Energy-efficient AI Hardware Design for IoT Applications

Lookup NU author(s): Adrian Wheeldon, Dr Rishad Shafik, Tousif Rahman, Jie lei, Professor Alex Yakovlev

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Abstract

Energy efficiency continues to be the core design challenge for artificial intelligence hardware (AI) designers. In this paper, we propose a new AI hardware architecture targeting IoT applications. The architecture is founded on the principle of learning automata, defined using propositional logic. The logic-based underpinning enables low energy footprints as well as high learning accuracy during training and inference, which are crucial requirements for efficient AI with long operating life. We present the first insights into this new architecture in the form of a custom-designed integrated circuit (IC) for pervasive applications. Fundamental to this circuit is systematic encoding of binarized input data fed into maximally parallel logic blocks. The allocation of these blocks is optimized through a design exploration and automation flow using FPGA-based fast prototypes and software simulations. The design flow allows for expedited hyperparameter search for meeting the conflicting requirements of energy frugality and high accuracy. Extensive validations on the hardware implementation of the new architecture using single and multi-class ML datasets show potential for significantly lower energy compared with the existing AI hardware architectures. In addition, we demonstrate test accuracy and robustness matching the software implementation, outperforming other state-of-the-art ML algorithms.


Publication metadata

Author(s): Wheeldon A, Shafik R, Rahman T, Lei J, Yakovlev A, Granmo OC

Publication type: Article

Publication status: In Press

Journal: Philosophical Transactions of the Royal Society A

Year: 2020

Pages: 1-18

Acceptance date: 03/06/2020

Date deposited: 13/07/2020

ISSN (print): 1471-2962

ISSN (electronic): 1364-503X

Publisher: The Royal Society


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