200 citations · 513 across the 24 of their papers we have counts for
3 papers · 1 filter
Feed-Forward On-Edge Fine-tuning Using Static Synthetic Gradient Modules
Robby Neven, Marian Verhelst, Tinne Tuytelaars +1
Training deep learning models on embedded devices is typically avoided since this requires more memory, computation and power over inference. In this work, we focus on lowering the…
ZigZag: A Memory-Centric Rapid DNN Accelerator Design Space Exploration Framework
Linyan Mei, Pouya Houshmand, Vikram Jain +2
Building efficient embedded deep learning systems requires a tight co-design between DNN algorithms, memory hierarchy, and dataflow. However, owing to the large degrees of freedom…
Benchmarking TinyML Systems: Challenges and Direction
Colby R. Banbury, Vijay Janapa Reddi, Max Lam +14
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued progress is limited by…