NX-CGRA: A Programmable Hardware Accelerator for Core Transformer Algorithms on Edge Devices

Abstract: The increasing diversity and complexity of transformer workloads at the edge present significant challenges in balancing performance, energy efficiency, and architectural flexibility. This paper introduces NX-CGRA, a programmable hardware accelerator designed to support a range of transformer inference algorithms, including both linear and non-linear functions. Unlike fixed-function accelerators optimized for narrow use cases, NX-CGRA employs a coarse-grained reconfigurable array (CGRA) architecture with software-driven programmability, enabling efficient execution across varied kernel patterns. The architecture is evaluated using representative benchmarks derived from real-world transformer models, demonstrating high overall efficiency and favorable energy-area tradeoffs across different classes of operations. These results indicate the potential of NX-CGRA as a scalable and adaptable hardware solution for edge transformer deployment under constrained power and silicon budgets.