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[2602.22352] GRAU: Generic Reconfigurable Activation Unit Design for Neural Network Hardware Accelerators
Summary
The paper presents GRAU, a Generic Reconfigurable Activation Unit designed for neural network hardware accelerators, which significantly reduces hardware costs and enhances efficiency through innovative piecewise linear fitting techniques.
Why It Matters
As neural networks grow in complexity, efficient hardware solutions become crucial for performance and cost-effectiveness. GRAU addresses the limitations of traditional activation units, offering a scalable and flexible alternative that can support mixed-precision quantization, making it relevant for edge computing applications.
Key Takeaways
- GRAU reduces LUT consumption by over 90% compared to multi-threshold activators.
- The design supports mixed-precision quantization and nonlinear functions.
- GRAU enhances hardware efficiency, flexibility, and scalability for neural network accelerators.
Computer Science > Hardware Architecture arXiv:2602.22352 (cs) [Submitted on 25 Feb 2026] Title:GRAU: Generic Reconfigurable Activation Unit Design for Neural Network Hardware Accelerators Authors:Yuhao Liu, Salim Ullah, Akash Kumar View a PDF of the paper titled GRAU: Generic Reconfigurable Activation Unit Design for Neural Network Hardware Accelerators, by Yuhao Liu and 2 other authors View PDF 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. Subjects: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22352 [cs.AR] (or arXiv:2602.22352v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2602.22352 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuhao Liu [view email] [v1] Wed, 25 Feb 2026 19:18:22 UTC (252 KB) Fu...
