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X-TIME: an In-Memory Engine for Accelerating Machine Learning on Tabular Data with CAMs.

Computing Research Repository (CoRR)(2023)

Artificial Intelligence Research Lab (AIRL)

Cited 1|Views71
Abstract
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.
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Ensemble Learning,Memory Applications,Regression,Hyperdimensional Computing,Extreme Learning Machine
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要点】:论文提出了一种名为X-TIME的内存内加速器,利用模拟内容寻址存储器(CAM)和可编程芯片,专门用于加速表格数据的机器学习任务,实现了在精确度、吞吐量、延迟和能效方面相对于现有GPU的显著提升。

方法】:作者开发了一种模拟-数字架构,其中包括一种新型高精度模拟CAM和一个用于推理最新树基机器学习模型(如XGBoost、CatBoost等)的可编程芯片,并采用了硬件感知训练方法。

实验】:研究通过使用未具体提及的表格数据集,证明了X-TIME在具有最多4096棵树和深度为8的模型上,与最先进的GPU相比,达到最新水平的准确度,同时实现了119倍的吞吐量提升、9740倍的延迟降低以及超过150倍的能效改进,且峰值功耗为19W。