Work

MonoSparse-CAM: Efficient Tree Model Processing via Monotonicity and Sparsity in CAMs

Year: 2025

Type: conference-paper

Abstract: While the tree-based machine learning (TBML) models exhibit superior performance compared to neural networks on tabular data and hold promise for energy-efficient acceleration using aCAM arrays, their ideal deployment on hardware with explicit exploitation of TBML structure and aCAM circuitry remains a challenging task. In this work, we present MonoSparse-CAM, a new CAM-based optimization technique that exploits TBML sparsity and monotonicity in CAM circuitry to further advance processing performance. Our results indicate that MonoSparse-CAM reduces energy consumption by upto to 28.56× compared to raw processing and by 18.51× compared to state-of-the-art techniques, while improving the efficiency of computation by at least 1.68×.

Source: IEEE International Symposium on Circuits and Systems proceedings

DOI: 10.1109/iscas56072.2025.11043997

Authors: Tergel Molom-Ochir, Brady Taylor, Hai Helen Li, Yiran Chen

Institution: Duke University

Language: English

Topic: Network Packet Processing and Optimization

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Division of Electrical, Communications and Cyber Systems

Awards: G1784431373, PARTNER: Neuro-Inspired AI for the Edge at UTSA (NAIAD), Collaborative Research: FuSe: Efficient Situation-Aware AI Processing in Advanced 2-Terminal SOT-MRAM, AI Institute for Edge Computing Leveraging Next Generation Networks (Athena), FuSe: Co-designed Systems for In-sensor Processing with Sustainable Nanomaterials (COSMIC)