A Kolmogorov–Arnold Compute-in-Memory (KA-CIM) Hardware Accelerator with High Energy Efficiency and Flexibility

Kolmogorov-Arnold Networks (KAN) are an emerging AI model designed for AI+Science applications, offering up to 100x fewer parameters than conventional Multilayer Perceptrons (MLPs). KAN relies on computationally expensive non-linear functions, unlike MLPs, which are dominated by matrix multiplication. This limits KAN's compatibility with energy-efficient hardware accelerators like Compute-in-Memory (CIM), restricting it to energy-inefficient general-purpose chips. To address this gap, we propose KA-CIM, a memory-centric design for energy-efficient computation of KAN inference. We leverage the Piece-Wise Linear (PWL) approximation of non-linear functions to convert complex computations into Multiply-Accumulate (MAC) operations and pre-storing segment parameters, an approach suitable for memory-centric design. A specialized CIM unit dynamically routes inputs to appropriate PWL segments, while a crossbar array retrieves segment slope and intercept to execute the MAC operations. The tile partitioning feature facilitates higher PWL segments for improved accuracy without significant energy penalties. This architectural design facilitates efficient and flexible computation of arbitrary non-linear functions of KANs. Beyond KAN inference, KA-CIM's capability extends to multi-variable equations and derivative computations. KA-CIM achieves energy-delay products 1073x lower than CPU for non-linear operations. When executing KAN, it achieves 77x lower energy-delay product than a 100 TOPS/W CIM accelerator executing MLP for the same task.