# X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs

arXiv: https://arxiv.org/abs/2304.01285 · HTML: https://mlsanity.com/paper/2304.01285/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2023.

## Built on by (most cited first, 4 of 4)

- [RACE-IT: A Reconfigurable Analog Computing Engine for In-Memory Transformer Acceleration](https://mlsanity.com/paper/2312.06532/) (2023)
- [RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory](https://mlsanity.com/paper/2506.05994/) (2025)
- [Trustworthy Tree-based Machine Learning by MoS2 Flash-based Analog CAM with Inherent Soft Boundaries](https://mlsanity.com/paper/2507.12384/) (2025)
- [A Fast and Energy-Efficient Latch-Based Memristive Analog Content-Addressable Memory](https://mlsanity.com/paper/2605.11847/) (2026)

## Abstract (arXiv)

Structured, or tabular, data is the most common format in data science. While deep learning models have proven formidable in learning from unstructured data such as images or speech, they are less accurate than simpler approaches when learning from tabular data. In contrast, modern tree-based Machine Learning (ML) models shine in extracting relevant information from structured data. An essential requirement in data science is to reduce model inference latency in cases where, for example, models are used in a closed loop with simulation to accelerate scientific discovery. However, the hardware acceleration community has mostly focused on deep neural networks and largely ignored other forms of machine learning. Previous work has described the use of an analog content addressable memory (CAM) component for efficiently mapping random forests. In this work, we develop an analog-digital architecture that implements a novel increased precision analog CAM and a programmable chip for inference of state-of-the-art tree-based ML models, such as XGBoost, CatBoost, and others. Thanks to hardware-aware training, X-TIME reaches state-of-the-art accuracy and 119x higher throughput at 9740x lower latency with >150x improved energy efficiency compared with a state-of-the-art GPU for models with up to 4096 trees and depth of 8, with a 19W peak power consumption.