  Despite significant advancements in AI driven by neural networks, tree-based
machine learning (TBML) models excel on tabular data. These models exhibit
promising energy efficiency, and high performance, particularly when
accelerated on analog content-addressable memory (aCAM) arrays. However,
optimizing their hardware deployment, especially in leveraging TBML model
structure and aCAM circuitry, remains challenging.
  In this paper, we introduce MonoSparse-CAM, a novel content-addressable
memory (CAM) based computing optimization technique. MonoSparse-CAM efficiently
leverages TBML model sparsity and CAM array circuits, enhancing processing
performance. Our experiments show that MonoSparse-CAM reduces energy
consumption by up to 28.56x compared to raw processing and 18.51x compared to
existing deployment optimization techniques. Additionally, it consistently
achieves at least 1.68x computational efficiency over current methods.
  By enabling energy-efficient CAM-based computing while preserving performance
regardless of the array sparsity, MonoSparse-CAM addresses the high energy
consumption problem of CAM which hinders processing of large arrays. Our
contributions are twofold: we propose MonoSparse-CAM as an effective deployment
optimization solution for CAM-based computing, and we investigate the impact of
TBML model structure on array sparsity. This work provides crucial insights for
energy-efficient TBML on hardware, highlighting a significant advancement in
sustainable AI technologies.
