1 citations · 3 across the 4 of their papers we have counts for
4 papers
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…