37 citations · 109 across the 8 of their papers we have counts for
5 papers · 1 filter
Compressed Recurrent Feedback in Tsetlin Machines: A Reproducible Boolean-FSM Study
Ankit Kumar, Utkarsh Raj, Rishad Shafik +1
Sequential inference on small devices requires a model to retain useful history without repeatedly processing a long input record. A Recurrent Tsetlin Machine (RTM) provides this m…
Neural Architecture Search using Property Guided Synthesis
Charles Jin, Phitchaya Mangpo Phothilimthana, Sudip Roy
In the past few years, neural architecture search (NAS) has become an increasingly important tool within the deep learning community. Despite the many recent successes of NAS, howe…
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules
Xinfeng Xie, Prakash Prabhu, Ulysse Beaugnon +6
Multi-Chip-Modules (MCMs) reduce the design and fabrication cost of machine learning (ML) accelerators while delivering performance and energy efficiency on par with a monolithic l…
Transferable Graph Optimizers for ML Compilers
Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi +9
Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code. Current ML compilers rely on heuristics…
GDP: Generalized Device Placement for Dataflow Graphs
Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi +8
Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices. With increasingly comple…