563 citations · 921 across the 26 of their papers we have counts for
4 papers · 1 filter
Rank and run-time aware compression of NLP Applications
Urmish Thakker, Jesse Beu, Dibakar Gope +2
Sequence model based NLP applications can be large. Yet, many applications that benefit from them run on small devices with very limited compute and storage capabilities, while sti…
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…
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
Ahmed Imteaj, Urmish Thakker, Shiqiang Wang +2
Nowadays, devices are equipped with advanced sensors with higher processing/computing capabilities. Further, widespread Internet availability enables communication among sensing de…
Compressing Language Models using Doped Kronecker Products
Urmish Thakker, Paul N. Whatmough, Zhi-Gang Liu +2
Kronecker Products (KP) have been used to compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods. However when K…