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

2026-02-25

AI Summary

This work addresses the prohibitive hardware cost of conventional multi-threshold activation units, whose resource requirements grow exponentially ($2^n$ thresholds) with increasing precision, thereby compromising efficiency and flexibility. To overcome this limitation, the authors propose GRAU, a reconfigurable activation unit that uniquely integrates piecewise linear approximation with power-of-two slope quantization. By leveraging only a comparator array and a single-bit right shifter, GRAU efficiently implements diverse nonlinear activation functions—such as SiLU—and supports mixed-precision quantization. This design drastically reduces hardware overhead while preserving functional versatility, achieving over 90% reduction in LUT consumption compared to traditional approaches, and consequently enhancing scalability and energy efficiency.

Technology Category

• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• neural network accelerators
• activation hardware
• low-precision quantization
• hardware cost
• multi-threshold activation

Innovation

• reconfigurable activation unit
• piecewise linear fitting
• low-precision quantization
• hardware efficiency
• neural network accelerator