PDR-KAN: Pipeline-Driven Reconfigurable Accelerator for Kolmogorov–Arnold Networks with Cross-Mode Sparsity Support

The commonly used Multi-layer Perceptrons (MLPs) in modern AI applications significantly limit the real-time performance due to their intensive memory access operation. Recently, Kolmogorov–Arnold Networks (KANs) have gained attention for offering a similar structure to MLPs but with a more efficient parameter utilization. However, the lack of customized support in conventional hardware restricts the performance gain from its algorithmic superiority. What’s more, the early-stage development of KAN raises questions about the necessity of incurring additional hardware costs for it. In this work, we present PDR-KAN, a reconfigurable accelerator that features two distinct operating modes, one for KANs and one for MLPs. Apart from its pipeline mode and sparsity encoder (SE) for efficient KAN processing, PDR-KAN can also improve the throughput of MLPs with cross-mode sparsity support. Experiments on a real-world dataset show that PDR-KAN provides a 3.95× acceleration and 13% reduction in accuracy loss by simply replacing MLPs with KANs. For a more accurate KAN model version with 3.33× parameter scaling, the latency overhead on PDR-KAN is only 1.16× compared to the baseline KAN model. Additionally, PDR-KAN achieves a 6.73× speed-up in KAN inferences and 20.89× in energy efficiency, against Quad-core ARM Cortex-A72 CPU.