7 citations · 15 across the 5 of their papers we have counts for
5 papers
MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure
Fangke Ye, Shengtian Zhou, Anand Venkat +10
Code semantics similarity can be used for many tasks such as code recommendation, automated software defect correction, and clone detection. Yet, the accuracy of such systems has n…
Context-Aware Parse Trees
Fangke Ye, Shengtian Zhou, Anand Venkat +8
The simplified parse tree (SPT) presented in Aroma, a state-of-the-art code recommendation system, is a tree-structured representation used to infer code semantics by capturing pro…
High-Performance Deep Learning via a Single Building Block
Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar +6
Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL librari…
ISA Mapper: A Compute and Hardware Agnostic Deep Learning Compiler
Matthew Sotoudeh, Anand Venkat, Michael Anderson +3
Domain specific accelerators present new challenges and opportunities for code generation onto novel instruction sets, communication fabrics, and memory architectures. In this pape…
Sparse Matrix Code Dependence Analysis Simplification at Compile Time
Mahdi Soltan Mohammadi, Kazem Cheshmi, Ganesh Gopalakrishnan +5
Analyzing array-based computations to determine data dependences is useful for many applications including automatic parallelization, race detection, computation and communication…