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REDRESS: Generating Compressed Models for Edge Inference Using Tsetlin Machines.

Sidharth Maheshwari, Tousif Rahman, Rishad Shafik

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    Summary
    This summary is machine-generated.

    This study introduces REDRESS, a novel method for training and inference of Tsetlin Machines (TMs) for edge computing. REDRESS significantly reduces memory footprint and boosts performance, outperforming Binary Neural Networks (BNNs) on benchmark datasets.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Edge computing faces trade-offs between resource efficiency (energy, memory) and performance (speed, accuracy) for embedded machine learning models.
    • Conventional approaches like Binary Neural Networks (BNNs) often struggle to balance these competing demands.
    • Tsetlin Machine (TM), an emerging algorithm using learning automata for propositional logic, offers a potential alternative.

    Purpose of the Study:

    • To propose a novel algorithm-hardware co-design methodology, REDRESS, for efficient training and inference of Tsetlin Machines (TMs) on resource-constrained edge devices.
    • To significantly reduce the memory footprint of TMs through lossless compression and optimized training.
    • To achieve high-speed inference without compromising accuracy, targeting low and ultra-low power applications.

    Main Methods:

    • Developed REDRESS, a methodology featuring independent TM training and inference techniques.
    • Introduced 'include-encoding' for lossless compression of Tsetlin Automata (TA), achieving over 99% compression by storing only 'include' information.
    • Implemented a computationally minimal training procedure, 'Tsetlin Automata Re-profiling', to enhance accuracy and TA sparsity.
    • Designed a bit-parallel inference algorithm operating directly on the compressed TA data, eliminating runtime decompression.

    Main Results:

    • The REDRESS methodology demonstrated superior performance of Tsetlin Machines (TMs) over state-of-the-art Binary Neural Networks (BNNs) across five benchmark datasets (MNIST, CIFAR2, KWS6, Fashion-MNIST, Kuzushiji-MNIST).
    • Achieved over 99% lossless compression for Tsetlin Automata (TA) using the 'include-encoding' method.
    • On an STM32F746G-DISCO microcontroller, REDRESS delivered speedups and energy savings ranging from 5x to 5700x compared to various BNN models.
    • The proposed training and inference techniques significantly reduced memory footprint and computation time.

    Conclusions:

    • REDRESS offers a highly efficient approach for deploying Tsetlin Machines (TMs) in edge computing environments, effectively addressing the resource-performance trade-offs.
    • The methodology, including lossless compression and bit-parallel inference, enables significant memory savings and substantial speedups compared to existing BNN models.
    • REDRESS paves the way for advanced machine learning capabilities in low-power and ultra-low-power embedded systems.