3 citations · 6 across the 13 of their papers we have counts for
8 papers · 1 filter
Inference Latency-Aware Tsetlin Machine Training
Yefan Zeng, Shengyu Duan, Rishad Shafik +1
The Tsetlin Machine (TM) is a logic-based machine learning model well suited to low-latency inference on resource-constrained edge devices. Because each clause is an AND over Boole…
CRISP: A Framework for Clause-Reconstructed Interpretable NeuroSymbolic Propositions
Alex Chan, Shafi Muhtasim Chowdhury, Ekin Can Erkuş +3
Deep neural networks achieve high accuracy through layered numerical transformations, yet their decisions remain difficult to audit because decision evidence is encoded in hidden a…
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…
Event-Driven Digital-Time-Domain Inference Architectures for Tsetlin Machines
Tian Lan, Rishad Shafik, Alex Yakovlev
Machine learning fits model parameters to approximate input-output mappings, predicting unknown samples. However, these models often require extensive arithmetic computations durin…
Uncertainty Quantification in the Tsetlin Machine
Runar Helin, Ole-Christoffer Granmo, Mayur Kishor Shende +5
Data modeling using Tsetlin machines (TMs) is all about building logical rules from the data features. The decisions of the model are based on a combination of these logical rules.…
Efficient FPGA Implementation of Time-Domain Popcount for Low-Complexity Machine Learning
Shengyu Duan, Marcos L. L. Sartori, Rishad Shafik +2
Population count (popcount) is a crucial operation for many low-complexity machine learning (ML) algorithms, including Tsetlin Machine (TM)-a promising new ML method, particularly…
ETHEREAL: Energy-efficient and High-throughput Inference using Compressed Tsetlin Machine
Shengyu Duan, Rishad Shafik, Alex Yakovlev
The Tsetlin Machine (TM) is a novel alternative to deep neural networks (DNNs). Unlike DNNs, which rely on multi-path arithmetic operations, a TM learns propositional logic pattern…
An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator
Svein Anders Tunheim, Yujin Zheng, Lei Jiao +3
We present an all-digital programmable machine learning accelerator chip for image classification, underpinning on the Tsetlin machine (TM) principles. The TM is an emerging machin…