Presentation
FeKAN: Efficient Kolmogorov-Arnold Networks Accelerator Using FeFET-based CAM and LUT
DescriptionKolmogorov-Arnold networks (KANs) have emerged as a promising alternative to MLP due to their adaptive learning capabilities for complex dependencies through B-spline basis activations (BBA). However, existing in-memory accelerators optimized for MLP-based DNNs are primarily designed for vector-matrix multiplication (VMM), making them inefficient for the dynamic and recursive B-spline interpolation (BSI) operations required by KANs. In this work, we propose FeKAN, an FeFET-based architecture designed to accelerate BBA operations. First, we develop a software-hardware co-optimized framework for mapping B-spline basis functions, leveraging a two-stage design space exploration (DSE) algorithm in combination with FeFET-based Look-Up Tables (LUT) and Content-Addressable Memory (CAM). This framework translated dynamic BSI operations into static codebook lookups, achieving a balanced trade-off between memory and computational efficiency. Second, we propose compress-sparsity-column (CSC) based encoding for B-spline basis function (BBF) and grouped-computation strategy for memory and energy reduction. Third, we propose a grouped-pipeline optimization strategy to mitigate data dependencies, significantly enhancing computation efficiency. Experimental results demonstrate that FeKAN achieves up to $150.68\text{K}\times$ and $4664\times$ higher throughput and up to $606.87\times$ and $11196\times$ greater energy efficiency over Intel Xeon Silver 4310 CPU and NVIDIA A6000 GPU, respectively.
Event Type
Research Manuscript
TimeMonday, June 231:30pm - 1:45pm PDT
Location3002, Level 3
Design
DES2B: In-memory and Near-memory Computing Architectures, Applications and Systems