Bitwidth-Specific Logarithmic Arithmetic for Future Hardware-Accelerated Training

• URL: http://arxiv.org/abs/2510.17058v1
• Date: Mon, 20 Oct 2025 00:25:07 GMT
• Title: Bitwidth-Specific Logarithmic Arithmetic for Future Hardware-Accelerated Training
• Authors: Hassan Hamad, Yuou Qiu, Peter A. Beerel, Keith M. Chugg,
• Abstract summary: Low-precision logarithmic fixed-point training presents a compelling alternative to complex floating-point arithmetic.<n>This work introduces a novel enhancement in low-precision logarithmic fixed-point training, geared towards future hardware accelerator designs.
• Score: 12.259268239255448
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While advancements in quantization have significantly reduced the computational costs of inference in deep learning, training still predominantly relies on complex floating-point arithmetic. Low-precision fixed-point training presents a compelling alternative. This work introduces a novel enhancement in low-precision logarithmic fixed-point training, geared towards future hardware accelerator designs. We propose incorporating bitwidth in the design of approximations to arithmetic operations. To this end, we introduce a new hardware-friendly, piece-wise linear approximation for logarithmic addition. Using simulated annealing, we optimize this approximation at different precision levels. A C++ bit-true simulation demonstrates training of VGG-11 and VGG-16 models on CIFAR-100 and TinyImageNet, respectively, using 12-bit integer arithmetic with minimal accuracy degradation compared to 32-bit floating-point training. Our hardware study reveals up to 32.5% reduction in area and 53.5% reduction in energy consumption for the proposed LNS multiply-accumulate units compared to that of linear fixed-point equivalents.

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