Deep random forest with ferroelectric analog content addressable memory | JoVE Visualize

Area of Science:

Artificial Intelligence
Computer Engineering
Materials Science

Background:

Deep Random Forest (DRF) models offer competitive accuracy and interpretability for edge intelligence tasks, similar to Deep Neural Networks (DNNs).
Existing hardware accelerators for DRF lag behind DNN counterparts, particularly in efficiently handling the crucial branch-split operations at decision nodes.
Ferroelectric materials offer unique properties for novel memory and computing architectures.

Purpose of the Study:

To propose and demonstrate an efficient hardware accelerator for Deep Random Forest (DRF) algorithms.
To leverage ferroelectric analog content addressable memory (ACAM) for accelerating DRF's decision-making processes.
To evaluate the performance, energy efficiency, and scalability of the proposed DRF accelerator architecture.

Main Methods:

Implementation of DRF using associative searches within ferroelectric analog content addressable memory (ACAM) cells.
Design of an ultra-compact ACAM cell utilizing two ferroelectric field-effect transistors (FeFETs) to store decision boundaries as analog polarization states.
Development of a DRF accelerator architecture and a methodology for mapping DRF models onto ACAM arrays.
Experimental and simulation-based validation of the FeFET ACAM DRF's functionality, characteristics, scalability, and robustness against device non-idealities.

Main Results:

The proposed FeFET ACAM DRF accelerator achieves significant improvements in energy efficiency (∼10^6×/10×) and latency (∼10^6×/2.5×) compared to state-of-the-art CPU and ReRAM-based DRF hardware implementations.
The ACAM cell enables energy-efficient branch-split operations by utilizing analog polarization states in FeFETs.
The architecture demonstrates robustness against FeFET device non-idealities, ensuring reliable operation.

Conclusions:

Ferroelectric ACAM provides a promising pathway for realizing ultra-compact and energy-efficient hardware accelerators for Deep Random Forest (DRF) algorithms.
The proposed FeFET ACAM DRF architecture offers substantial performance gains, addressing the current limitations in DRF hardware acceleration for edge intelligence.
This work paves the way for next-generation edge AI systems with enhanced computational capabilities and reduced power consumption.