VLSI Tree-Based Inference Design Applications for Low-Power Learning

Nagaraju, V and Suresh, G and Uthayakumar, C and Jijina, G O (2021) VLSI Tree-Based Inference Design Applications for Low-Power Learning. Journal of Physics: Conference Series, 1964 (6). 062047. ISSN 1742-6588

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Abstract

For the decision tree ensemble, this paper suggests a hardware architecture utilizing many feature channels. The proposed work uses the complexity of function channels for rapid identification compared to parallel processing in spatial domain scheduling to achieve conflict-free system memory. The results' analysis demonstrates that only an FPGA implementation of the new architecture with a pedestrian sensor collated channel feature will conduct 229 thousand pulses per second at an operational value of 100 MHz while providing relatively limited resources. Checking estimation systems' electricity-accuracy trade-offs has become central. This research evaluates the nature of data sets, investigating the outcomes of design difficulty or intensity of accuracy approximation. We improved the simulations' precision by up to 6.7 percent by quantizing the inputs to small sizes relative to specific scenarios. The gap in model complexity was more important than source distance in terms of capacity, as we achieved reductions of up to 67 percent by reducing tree depth. © 2021 Elsevier B.V., All rights reserved.

Item Type: Article
Subjects: Computer Science > Hardware and Architecture
Divisions: Engineering and Technology > Aarupadai Veedu Institute of Technology, Chennai > Electronics & Communication Engineering
Depositing User: Unnamed user with email techsupport@mosys.org
Last Modified: 03 Dec 2025 12:08
URI: https://vmuir.mosys.org/id/eprint/3145

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