NAND-Tree: A 3D NAND Flash Based Processing In Memory Accelerator for Tree-Based Models on Large-Scale Tabular Data
Year: 2024
Type: conference-paper
Abstract: Tabular data are a widely used format in data science, and tree-based Machine Learning (ML) models are powerful tools and outperform Deep Neural Network (DNN) with higher accuracy for tasks on tabular data. However, computing multiple trees on massive tabular data via conventional von Neumann architectures suffers from irregular memory accesses. Prior work utilizes Analog Content Addressable Memories (ACAMs) to gain great speedup, but the analog matching method is vulnerable to device and voltage variations, and the limited density of 2D memory makes frequent data movement still inevitable for large-scale tabular data.
Institution: Tsinghua University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Awards: G1436115020, G5369691468
OpenAlex ID: W4409285560