GRAU: Generic Reconfigurable Activation Unit Design for Neural Network Hardware Accelerators

The paper presents GRAU, a Generic Reconfigurable Activation Unit designed for neural network hardware accelerators, which utilizes piecewise linear fitting with segment slopes approximated by powers of two. This design significantly reduces lookup table (LUT) consumption by over 90% compared to traditional multi-threshold activation hardware, while supporting mixed-precision quantization and nonlinear functions like SiLU. GRAU's efficiency and scalability are critical for practitioners working with low-precision quantization in edge AI applications, allowing for more flexible and cost-effective neural network implementations.