28 citations · 45 across the 5 of their papers we have counts for
6 papers
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit
Nikhil P Ghanathe, Steve Wilton
Deploying Machine learning (ML) on milliwatt-scale edge devices (tinyML) is gaining popularity due to recent breakthroughs in ML and Internet of Things (IoT). Most tinyML research…
MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications
Nikhil Pratap Ghanathe, Vivek Seshadri, Rahul Sharma +2
Recent breakthroughs in ML have produced new classes of models that allow ML inference to run directly on milliwatt-powered IoT devices. On one hand, existing ML-to-FPGA compilers…
LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks
Daniel H. Noronha, Bahar Salehpour, Steven J. E. Wilton
Recent work has shown that Field-Programmable Gate Arrays (FPGAs) play an important role in the acceleration of Machine Learning applications. Initial specification of machine lear…
Algorithms for Embedding Quantum-Dot Cellular Automata Networks onto a Quantum Annealing Processor
Jacob Retallick, Michael Babcock, Miguel Aroca-Ouellette +5
Advancements in computing based on qubit networks, and in particular the flux-qubit processor architecture developed by D-Wave System's Inc., have enabled the physical simulation o…
Enabling Effective FPGA Debug using Overlays: Opportunities and Challenges
Fatemeh Eslami, Eddie Hung, Steven J. E. Wilton
FPGAs are going mainstream. Major companies that were not traditionally FPGA-focused are now seeking ways to exploit the benefits of reconfigurable technology and provide it to the…
Allowing Software Developers to Debug HLS Hardware
Jeffrey Goeders, Steven J. E. Wilton
High-Level Synthesis (HLS) is emerging as a mainstream design methodology, allowing software designers to enjoy the benefits of a hardware implementation. Significant work has led…