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

Researchers propose GRAU, a new reconfigurable activation unit design for neural network hardware accelerators that uses piecewise linear fitting with power-of-two slopes. The design reduces LUT consumption by over 90% compared to traditional multi-threshold activators while supporting mixed-precision quantization and nonlinear functions.

• →GRAU addresses the exponential hardware cost growth of classic multi-threshold activation units that require 2^n thresholds for n-bit outputs.
• →The design uses only basic comparators and 1-bit right shifters, making it highly efficient for edge computing applications.
• →GRAU supports mixed-precision quantization and nonlinear functions like SiLU, providing greater flexibility than traditional approaches.
• →The hardware achieves over 90% reduction in LUT consumption while maintaining scalability for growing neural network sizes.
• →This innovation could significantly improve the efficiency of AI hardware accelerators used in edge devices and low-power applications.