GRAU: Generic Reconfigurable Activation Unit Design for Neural Network Hardware Accelerators
#GRAU #Neural Network Accelerators #Hardware Architecture #Low-Precision Quantization #Multi-Threshold Activation #Edge Computing #Reconfigurable Hardware
📌 Key Takeaways
- Researchers developed GRAU to address hardware cost issues in neural network accelerators
- Traditional multi-threshold activation hardware requires exponential resources with increasing precision
- GRAU uses piecewise linear fitting with power-of-two slopes for efficient computation
- The design reduces LUT consumption by over 90% while supporting mixed-precision quantization
📖 Full Retelling
Researchers Yuhao Liu, Salim Ullah, and Akash Kumar introduced GRAU, a Generic Reconfigurable Activation Unit design for neural network hardware accelerators, in a paper published on arXiv on February 25, 2026, addressing the growing hardware costs associated with increasing neural network precision requirements. With neural networks continuously growing in scale, low-precision quantization has become essential for edge accelerators, but traditional multi-threshold activation hardware faces significant limitations as it requires 2^n thresholds for n-bit outputs, causing exponential increases in hardware costs with each bit of precision added. The GRAU design proposes an innovative approach using piecewise linear fitting where segment slopes are approximated by powers of two, dramatically reducing the hardware complexity while maintaining computational accuracy. This breakthrough architecture requires only basic comparators and 1-bit right shifters, making it remarkably efficient and versatile for various neural network applications. The research team demonstrated that GRAU not only supports mixed-precision quantization but can also implement complex nonlinear functions such as SiLU (Sigmoid Linear Unit), which is commonly used in modern neural networks. Compared to conventional multi-threshold activators, GRAU achieves over 90% reduction in LUT (Look-Up Table) consumption, representing a significant advancement in hardware efficiency, flexibility, and scalability for edge computing devices where power and resource constraints are critical factors.
🏷️ Themes
Hardware Architecture, Artificial Intelligence, Edge Computing
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Original Source
--> Computer Science > Hardware Architecture arXiv:2602.22352 [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) Full-text links: Access Paper: 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 TeX Source view license Current browse context: cs.AR < prev | next > new | recent | 2026-02 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Too...
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