Tree-based machine learning performed in-memory with memristive analog CAM

TL;DR

This paper introduces a novel in-memory, analog CAM-based hardware architecture that accelerates tree-based machine learning inference, achieving significant energy efficiency improvements over traditional accelerators.

Contribution

It presents a new analog CAM design and architecture for rapid, energy-efficient inference of large tree-based models, outperforming existing solutions.

Findings

Orders of magnitude energy reduction in inference

Effective compression of large models

Potential for hardware acceleration of tree-based ML

Abstract

Tree-based machine learning techniques, such as Decision Trees and Random Forests, are top performers in several domains as they do well with limited training datasets and offer improved interpretability compared to Deep Neural Networks (DNN). However, while easier to train, they are difficult to optimize for fast inference without accuracy loss in von Neumann architectures due to non-uniform memory access patterns. Recently, we proposed a novel analog, or multi-bit, content addressable memory(CAM) for fast look-up table operations. Here, we propose a design utilizing this as a computational primitive for rapid tree-based inference. Large random forest models are mapped to arrays of analog CAMs coupled to traditional analog random access memory (RAM), and the unique features of the analog CAM enable compression and high performance. An optimized architecture is compared with previously proposed tree-based model accelerators, showing improvements in energy to decision by orders of magnitude for common image classification tasks. The results demonstrate the potential for non-volatile analog CAM hardware in accelerating large tree-based machine learning models.