5 papers · 1 filter
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…
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…
Fast and Compact Tsetlin Machine Inference on CPUs Using Instruction-Level Optimization
Yefan Zeng, Shengyu Duan, Rishad Shafik +1
The Tsetlin Machine (TM) offers high-speed inference on resource-constrained devices such as CPUs. Its logic-driven operations naturally lend themselves to parallel execution on mo…
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…