| Autor: |
Yin X; Zhejiang University, Hangzhou, Zhejiang, China.; Key Laboratory of CS&AUS of Zhejiang Province, Hangzhou, China., Müller F; Fraunhofer IPMS, Dresden, Germany., Laguna AF; De La Salle University, Manila, Philippines., Li C; Zhejiang University, Hangzhou, Zhejiang, China., Huang Q; Zhejiang University, Hangzhou, Zhejiang, China., Shi Z; Zhejiang University, Hangzhou, Zhejiang, China.; Key Laboratory of CS&AUS of Zhejiang Province, Hangzhou, China., Lederer M; Fraunhofer IPMS, Dresden, Germany., Laleni N; Fraunhofer IPMS, Dresden, Germany., Deng S; University of Notre Dame, Notre Dame, IN 46614, USA., Zhao Z; University of Notre Dame, Notre Dame, IN 46614, USA., Imani M; University of California, Irvine, CA 92697, USA., Shi Y; University of Notre Dame, Notre Dame, IN 46614, USA., Niemier M; University of Notre Dame, Notre Dame, IN 46614, USA., Hu XS; University of Notre Dame, Notre Dame, IN 46614, USA., Zhuo C; Zhejiang University, Hangzhou, Zhejiang, China.; Key Laboratory of CS&AUS of Zhejiang Province, Hangzhou, China., Kämpfe T; Fraunhofer IPMS, Dresden, Germany., Ni K; University of Notre Dame, Notre Dame, IN 46614, USA. |
| Abstrakt: |
Deep random forest (DRF), which combines deep learning and random forest, exhibits comparable accuracy, interpretability, low memory and computational overhead to deep neural networks (DNNs) in edge intelligence tasks. However, efficient DRF accelerator is lagging behind its DNN counterparts. The key to DRF acceleration lies in realizing the branch-split operation at decision nodes. In this work, we propose implementing DRF through associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell performs energy-efficient branch-split operations by storing decision boundaries as analog polarization states in FeFETs. The DRF accelerator architecture and its model mapping to ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM DRF and its robustness against FeFET device non-idealities are validated in experiments and simulations. Evaluations show that the FeFET ACAM DRF accelerator achieves ∼10 6 ×/10× and ∼10 6 ×/2.5× improvements in energy and latency, respectively, compared to other DRF hardware implementations on state-of-the-art CPU/ReRAM. |
