Signed Binarization: Unlocking Efficiency Through Repetition-Sparsity Trade-Off

• URL: http://arxiv.org/abs/2312.01581v1
• Date: Mon, 4 Dec 2023 02:33:53 GMT
• Title: Signed Binarization: Unlocking Efficiency Through Repetition-Sparsity Trade-Off
• Authors: Sachit Kuhar and Yash Jain and Alexey Tumanov
• Abstract summary: This paper introduces the concept of repetition-sparsity trade-off that helps explain computational efficiency during inference. We propose Signed Binarization, a unified co-design framework that integrates hardware-software systems, quantization functions, and representation learning techniques to address this trade-off. Our approach achieves a 26% speedup on real hardware, doubles energy efficiency, and reduces density by 2.8x compared to binary methods for ResNet 18.
• Score: 2.6144163646666945
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Efficient inference of Deep Neural Networks (DNNs) on resource-constrained edge devices is essential. Quantization and sparsity are key algorithmic techniques that translate to repetition and sparsity within tensors at the hardware-software interface. This paper introduces the concept of repetition-sparsity trade-off that helps explain computational efficiency during inference. We propose Signed Binarization, a unified co-design framework that synergistically integrates hardware-software systems, quantization functions, and representation learning techniques to address this trade-off. Our results demonstrate that Signed Binarization is more accurate than binarization with the same number of non-zero weights. Detailed analysis indicates that signed binarization generates a smaller distribution of effectual (non-zero) parameters nested within a larger distribution of total parameters, both of the same type, for a DNN block. Finally, our approach achieves a 26% speedup on real hardware, doubles energy efficiency, and reduces density by 2.8x compared to binary methods for ResNet 18, presenting an alternative solution for deploying efficient models in resource-limited environments.

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