  Deep random forest (DRF), which incorporates the core features of deep
learning and random forest (RF), exhibits comparable classification accuracy,
interpretability, and low memory and computational overhead when compared with
deep neural networks (DNNs) in various information processing tasks for edge
intelligence. However, the development of efficient hardware to accelerate DRF
is lagging behind its DNN counterparts. The key for hardware acceleration of
DRF lies in efficiently realizing the branch-split operation at decision nodes
when traversing a decision tree. In this work, we propose to implement DRF
through simple associative searches realized with ferroelectric analog content
addressable memory (ACAM). Utilizing only two ferroelectric field effect
transistors (FeFETs), the ultra-compact ACAM cell can perform a branch-split
operation with an energy-efficient associative search by storing the decision
boundaries as the analog polarization states in an FeFET. The DRF accelerator
architecture and the corresponding mapping of the DRF model to the ACAM arrays
are presented. The functionality, characteristics, and scalability of the FeFET
ACAM based DRF and its robustness against FeFET device non-idealities are
validated both in experiments and simulations. Evaluation results show that the
FeFET ACAM DRF accelerator exhibits 10^6x/16x and 10^6x/2.5x improvements in
terms of energy and latency when compared with other deep random forest
hardware implementations on the state-of-the-art CPU/ReRAM, respectively.
