7 papers
A Real-Time Tsetlin Machine-based Non-intrusive Load Monitoring System on MCUs
Tianhang Tan, Han Wu, Tousif Rahman +3
Non-Intrusive Load Monitoring (NILM) systems estimate individual appliance energy consumption from a single aggregate meter, without requiring separate sensors for each device. By…
Low-Energy Reduced RISC-V Instruction Subset Processor for Tsetlin Machine Inference at the Edge
Chanda Gupta, Sanidhya Bhatia, Shaurya Priyadarshi +3
Tsetlin Machine (TM) is a logic-based machine learning approach that relies on simple bitwise operations and finite-state automata, which makes it attractive for edge AI deployment…
FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +2
Embedding models in natural language processing (NLP) increasingly rely on deep architectures such as BERT, while simpler models such as Word2Vec provide efficient representations…
TsetlinWiSARD: On-Chip Training of Weightless Neural Networks using Tsetlin Automata on FPGAs
Shengyu Duan, Marcos L. L. Sartori, Rishad Shafik +1
Increasing demands for adaptability, privacy, and security at the edge have persistently pushed the frontiers for a new generation of machine learning (ML) algorithms with training…
Eventizing Traditionally Opaque Binary Neural Networks as 1-safe Petri net Models
Mohamed Tarraf, Alex Chan, Alex Yakovlev +1
Binary Neural Networks (BNNs) offer a low-complexity and energy-efficient alternative to traditional full-precision neural networks by constraining their weights and activations to…
TsetlinKWS: A 65nm 16.58uW, 0.63mm2 State-Driven Convolutional Tsetlin Machine-Based Accelerator For Keyword Spotting
Baizhou Lin, Yuetong Fang, Renjing Xu +2
The Tsetlin Machine (TM) has recently attracted attention as a low-power alternative to neural networks due to its simple and interpretable inference mechanisms. However, its perfo…