708 citations · 1.2k across the 9 of their papers we have counts for
14 papers
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search
Thanh Vu, Yanqi Zhou, Chunfeng Wen +2
In this work, we propose a novel and scalable solution to address the challenges of developing efficient dense predictions on edge platforms. Our first key insight is that MultiTas…
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs
Berkin Akin, Suyog Gupta, Yun Long +6
On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high co…
LaMDA: Language Models for Dialog Applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall +57
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters an…
Rethinking Co-design of Neural Architectures and Hardware Accelerators
Yanqi Zhou, Xuanyi Dong, Berkin Akin +7
Neural architectures and hardware accelerators have been two driving forces for the progress in deep learning. Previous works typically attempt to optimize hardware given a fixed m…
Apollo: Transferable Architecture Exploration
Amir Yazdanbakhsh, Christof Angermueller, Berkin Akin +7
The looming end of Moore's Law and ascending use of deep learning drives the design of custom accelerators that are optimized for specific neural architectures. Architecture explor…
Do Transformer Modifications Transfer Across Implementations and Applications?
Sharan Narang, Hyung Won Chung, Yi Tay +13
The research community has proposed copious modifications to the Transformer architecture since it was introduced over three years ago, relatively few of which have seen widespread…