The Need
As data rates and storage densities continue to increase across communication and storage systems, the demand for fast, power-efficient error correction becomes more critical. Traditional Chien search architectures, used in Reed-Solomon decoders, are often computationally intensive and inefficient when scaled. There is a need for low-complexity, parallelizable architectures that reduce latency and hardware overhead without compromising decoding performance.

The Technology
This novel architecture introduces a parallel Chien search design based on Vandermonde matrix decomposition. By leveraging inherent matrix structure, the design significantly simplifies the evaluation of error locator polynomials, enabling parallel processing with reduced circuit complexity. The approach avoids redundant computation and reduces the number of required multipliers and adders, making it well-suited for high-speed, resource-constrained environments such as FPGA and ASIC implementations.

Commercial Applications
• High-speed communication systems (e.g., 5G, satellite, optical)
• Solid-state drives and memory controllers
• Data centers and cloud storage infrastructure
• IoT and embedded systems with constrained hardware
• Automotive and aerospace communication electronics

Benefits/Advantages
• Reduced computational complexity and gate count
• Enables parallel processing for high throughput
• Lower power consumption compared to conventional designs
• Scalable and adaptable to different code lengths
• Compatible with existing Reed-Solomon decoders and standards