3 citations · 6 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…