3 citations · 5 across the 4 of their papers we have counts for
6 papers
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…
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…
Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support
Sanket Tavarageri, Srinivas Sridharan, Bharat Kaul
The deep neural networks (DNNs) have been enormously successful in tasks that were hitherto in the human-only realm such as image recognition, and language translation. Owing to th…
Categorization of Program Regions for Agile Compilation using Machine Learning and Hardware Support
Sanket Tavarageri
A compiler processes the code written in a high level language and produces machine executable code. The compiler writers often face the challenge of keeping the compilation times…
A Data Analytics Framework for Aggregate Data Analysis
Sanket Tavarageri, Nag Mani, Anand Ramasubramanian +1
In many contexts, we have access to aggregate data, but individual level data is unavailable. For example, medical studies sometimes report only aggregate statistics about disease…