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BiKA: Kolmogorov-Arnold-Network-inspired Ultra Lightweight Neural Network Hardware Accelerator

arXiv:2602.23455v1 Announce Type: cross Abstract: Lightweight neural network accelerators are essential for edge devices with limited resources and power constraints. While quantization and binarization can efficiently reduce hardware cost, they still rely on the conventional Artificial Neural Network (ANN) computation pattern. The recently proposed Kolmogorov-Arnold Network (KAN) presents a novel network paradigm built on learnable nonlinear functions. However, it is computationally expensive for hardware deployment. Inspired by KAN, we propose BiKA, a multiply-free architecture that replaces nonlinear functions with binary, learnable thresholds, introducing an extremely lightweight computational pattern that requires only comparators and accumulators. Our FPGA prototype on Ultra96-V2 shows that BiKA reduces hardware resource usage by 27.73% and 51.54% compared with binarized and quantized neural network systolic array accelerators, while maintaining competitive accuracy. BiKA provid

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Yuhao Liu, Salim Ullah, Akash Kumar · · 1 min read · 11 views
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arXiv:2602.23455v1 Announce Type: cross Abstract: Lightweight neural network accelerators are essential for edge devices with limited resources and power constraints. While quantization and binarization can efficiently reduce hardware cost, they still rely on the conventional Artificial Neural Network (ANN) computation pattern. The recently proposed Kolmogorov-Arnold Network (KAN) presents a novel network paradigm built on learnable nonlinear functions. However, it is computationally expensive for hardware deployment. Inspired by KAN, we propose BiKA, a multiply-free architecture that replaces nonlinear functions with binary, learnable thresholds, introducing an extremely lightweight computational pattern that requires only comparators and accumulators. Our FPGA prototype on Ultra96-V2 shows that BiKA reduces hardware resource usage by 27.73% and 51.54% compared with binarized and quantized neural network systolic array accelerators, while maintaining competitive accuracy. BiKA provides a promising direction for hardware-friendly neural network design on edge devices.

Executive Summary

This article proposes BiKA, a novel neural network hardware accelerator inspired by the Kolmogorov-Arnold Network (KAN). BiKA replaces nonlinear functions with binary, learnable thresholds, resulting in an extremely lightweight computational pattern that requires only comparators and accumulators. The authors demonstrate the efficacy of BiKA on an FPGA prototype, showcasing a 27.73% and 51.54% reduction in hardware resource usage compared to binarized and quantized neural network systolic array accelerators. BiKA maintains competitive accuracy, providing a promising direction for hardware-friendly neural network design on edge devices. The proposed architecture has significant implications for the development of lightweight neural network accelerators, particularly for edge computing applications with limited resources and power constraints.

Key Points

  • ▸ BiKA is a novel neural network hardware accelerator inspired by KAN.
  • ▸ BiKA replaces nonlinear functions with binary, learnable thresholds.
  • ▸ BiKA demonstrates a significant reduction in hardware resource usage on an FPGA prototype.

Merits

Scalability

BiKA's lightweight computational pattern can be easily scaled to accommodate various neural network architectures, making it a promising solution for edge devices.

Energy Efficiency

BiKA's reduced hardware resource usage leads to lower power consumption, making it suitable for applications with energy constraints.

Demerits

Training Complexity

The training process for BiKA may be more complex due to the introduction of binary, learnable thresholds, which could impact its usability in real-world applications.

Hardware Requirements

BiKA may require specialized hardware to accommodate its unique computational pattern, which could be a limiting factor for widespread adoption.

Expert Commentary

The proposed BiKA architecture is a significant advancement in the field of neural network hardware accelerators. By leveraging the principles of KAN, BiKA provides a novel solution for edge devices with limited resources and power constraints. While there are some limitations to consider, the potential benefits of BiKA make it a promising direction for future research. In particular, the scalability and energy efficiency of BiKA make it an attractive solution for various edge computing applications. As the field continues to evolve, it will be essential to explore the implications of BiKA and similar architectures on the broader ecosystem of edge computing devices.

Recommendations

  • ✓ Further research is needed to explore the training complexities and hardware requirements of BiKA.
  • ✓ The development of BiKA-inspired architectures should prioritize scalability, energy efficiency, and usability in real-world applications.

Sources

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