4 citations · 7 across the 8 of their papers we have counts for
8 papers
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…
Dynamic Tsetlin Machine Accelerators for On-Chip Training at the Edge using FPGAs
Gang Mao, Tousif Rahman, Sidharth Maheshwari +4
The increased demand for data privacy and security in machine learning (ML) applications has put impetus on effective edge training on Internet-of-Things (IoT) nodes. Edge training…
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…
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…
An FPGA Architecture for Online Learning using the Tsetlin Machine
Samuel Prescott, Adrian Wheeldon, Rishad Shafik +3
There is a need for machine learning models to evolve in unsupervised circumstances. New classifications may be introduced, unexpected faults may occur, or the initial dataset may…
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…
Energy-frugal and Interpretable AI Hardware Design using Learning Automata
Rishad Shafik, Tousif Rahman, Adrian Wheeldon +2
Energy efficiency is a crucial requirement for enabling powerful artificial intelligence applications at the microedge. Hardware acceleration with frugal architectural allocation i…
Low-Power Audio Keyword Spotting using Tsetlin Machines
Jie Lei, Tousif Rahman, Rishad Shafik +5
The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy ef…