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.

翻译：轻量级神经网络加速器对于资源受限、功耗受限的边缘设备至关重要。虽然量化和二值化能有效降低硬件成本，但它们仍依赖于传统人工神经网络的计算模式。近期提出的Kolmogorov-Arnold网络（KAN）构建了一种基于可学习非线性函数的新型网络范式，但其计算开销较大，难以直接硬件部署。受KAN启发，我们提出BiKA——一种无乘法架构，通过二进制可学习阈值替代非线性函数，引入了一种仅需比较器和累加器的极轻量计算模式。我们在Ultra96-V2平台上的FPGA原型实验表明，与二值化及量化神经网络脉动阵列加速器相比，BiKA可分别降低27.73%和51.54%的硬件资源占用，同时保持具有竞争力的精度。BiKA为边缘设备的硬件友好型神经网络设计提供了新方向。