3 citations · 6 across the 13 of their papers we have counts for
15 papers
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…
Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs
Tousif Rahman, Gang Mao, Bob Pattison +5
Embedded Field-Programmable Gate Arrays (eFPGAs) allow for the design of hardware accelerators of edge Machine Learning (ML) applications at a lower power budget compared with trad…
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…
In-Memory Learning Automata Architecture using Y-Flash Cell
Omar Ghazal, Tian Lan, Shalman Ojukwu +3
The modern implementation of machine learning architectures faces significant challenges due to frequent data transfer between memory and processing units. In-memory computing, pri…
Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method)
Jordan Morris
This paper proposes a machine learning pre-sort stage to traditional supervised learning using Tsetlin Machines. Initially, K data-points are identified from the dataset using an e…
MATADOR: Automated System-on-Chip Tsetlin Machine Design Generation for Edge Applications
Tousif Rahman, Gang Mao, Sidharth Maheshwari +2
System-on-Chip Field-Programmable Gate Arrays (SoC-FPGAs) offer significant throughput gains for machine learning (ML) edge inference applications via the design of co-processor ac…
Contracting Tsetlin Machine with Absorbing Automata
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao +4
In this paper, we introduce a sparse Tsetlin Machine (TM) with absorbing Tsetlin Automata (TA) states. In brief, the TA of each clause literal has both an absorbing Exclude- and an…
IMBUE: In-Memory Boolean-to-CUrrent Inference ArchitecturE for Tsetlin Machines
Omar Ghazal, Simranjeet Singh, Tousif Rahman +8
In-memory computing for Machine Learning (ML) applications remedies the von Neumann bottlenecks by organizing computation to exploit parallelism and locality. Non-volatile memory d…
Finite State Automata Design using 1T1R ReRAM Crossbar
Simranjeet Singh, Omar Ghazal, Chandan Kumar Jha +6
Data movement costs constitute a significant bottleneck in modern machine learning (ML) systems. When combined with the computational complexity of algorithms, such as neural netwo…
An Event-Driven Approach To Genotype Imputation On A Custom RISC-V FPGA Cluster
Jordan Morris, Ashur Rafiev, Graeme Bragg +4
This paper proposes an event-driven solution to genotype imputation, a technique used to statistically infer missing genetic markers in DNA. The work implements the widely accepted…