66 citations · 230 across the 11 of their papers we have counts for
18 papers
Efficient and Generic 1D Dilated Convolution Layer for Deep Learning
Narendra Chaudhary, Sanchit Misra, Dhiraj Kalamkar +5
Convolutional neural networks (CNNs) have found many applications in tasks involving two-dimensional (2D) data, such as image classification and image processing. Therefore, 2D con…
AI Powered Compiler Techniques for DL Code Optimization
Sanket Tavarageri, Gagandeep Goyal, Sasikanth Avancha +2
Creating high performance implementations of deep learning primitives on CPUs is a challenging task. Multiple considerations including multi-level cache hierarchy, and wide SIMD un…
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks
Jacob R. Stevens, Dipankar Das, Sasikanth Avancha +2
Graph Neural Networks (GNNs) use a fully-connected layer to extract features from the nodes of a graph and aggregate these features using message passing between nodes, combining t…
MADRaS : Multi Agent Driving Simulator
Anirban Santara, Sohan Rudra, Sree Aditya Buridi +4
In this work, we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. MADRaS prov…
PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives
Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha +3
Deep Neural Networks (DNNs) have revolutionized many aspects of our lives. The use of DNNs is becoming ubiquitous including in softwares for image recognition, speech recognition,…
PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives
Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha +3
At the heart of deep learning training and inferencing are computationally intensive primitives such as convolutions which form the building blocks of deep neural networks. Researc…