7 papers · 1 filter
Compressed Recurrent Feedback in Tsetlin Machines: A Reproducible Boolean-FSM Study
Ankit Kumar, Utkarsh Raj, Rishad Shafik +1
Sequential inference on small devices requires a model to retain useful history without repeatedly processing a long input record. A Recurrent Tsetlin Machine (RTM) provides this m…
A Real-Time Tsetlin Machine-based Non-intrusive Load Monitoring System on MCUs
Han Wu, Tianhang Tan, Shengyu Duan +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…