With the continuous growth of neural network scales, low-precision quantization is widely used in edge accelerators. Classic multi-threshold activation hardware requires 2^n thresholds for $n$-bit outputs, causing a rapid increase in hardware cost as precision increases. We propose a reconfigurable activation hardware, GRAU, based on piecewise linear fitting, where the segment slopes are approximated by powers of two. Our design requires only basic comparators and 1-bit right shifters, supporting mixed-precision quantization and nonlinear functions such as SiLU. Compared with multi-threshold activators, GRAU reduces LUT consumption by over 90%, achieving higher hardware efficiency, flexibility, and scalability. The best trade-off is usually achieved with 6-8 segments, while complex nonlinearities under aggressive low-cost settings may suffer larger accuracy degradation.

翻译：随着神经网络规模的持续增长，低精度量化被广泛应用于边缘加速器中。经典的多阈值激活硬件需要2^n个阈值来实现n位输出，导致硬件成本随精度提升迅速增加。我们提出一种基于分段线性拟合的可重构激活硬件GRAU，其中各段斜率通过移位近似实现。该设计仅需基本比较器和1位右移器，支持混合精度量化及SiLU等非线性函数。与多阈值激活器相比，GRAU可减少90%以上的查找表消耗，实现更高的硬件效率、灵活性与可扩展性。通常6-8个分段可获得最佳权衡，但在激进低开销设置下，复杂非线性函数可能面临更大的精度损失。