2 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 2 cited
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions
Vinod Ganesan, Gowtham Ramesh, Pratyush Kumar
Task-agnostic pre-training followed by task-specific fine-tuning is a default approach to train NLU models. Such models need to be deployed on devices across the cloud and the edge…
cs.AR2021
Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge
Vinod Ganesan, Pratyush Kumar
Massively parallel systolic arrays and resource-efficient depthwise separable convolutions are two promising techniques to accelerate DNN inference on the edge. Interestingly, thei…
cs.AR2021★ 2 cited
FuSeConv: Fully Separable Convolutions for Fast Inference on Systolic Arrays
Surya Selvam, Vinod Ganesan, Pratyush Kumar
Both efficient neural networks and hardware accelerators are being explored to speed up DNN inference on edge devices. For example, MobileNet uses depthwise separable convolution t…