Purvish Jajal

According to our database, Purvish Jajal authored at least 9 papers between 2023 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Token Turing Machines are Efficient Vision Models.

Purvish Jajal, Nick John Eliopoulos, Benjamin Shiue-Hal Chou, George K. Thiravathukal, James C. Davis, Yung-Hsiang Lu

CoRR, 2024

Pruning One More Token is Enough: Leveraging Latency-Workload Non-Linearities for Vision Transformers on the Edge.

Nick John Eliopoulos, Purvish Jajal, James C. Davis, Gaowen Liu, George K. Thiravathukal, Yung-Hsiang Lu

CoRR, 2024

Interoperability in Deep Learning: A User Survey and Failure Analysis of ONNX Model Converters.

Purvish Jajal, Wenxin Jiang, Arav Tewari, Erik Kocinare, Joseph Woo, Anusha Sarraf, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis

Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2024

Securing Deep Neural Networks on Edge from Membership Inference Attacks Using Trusted Execution Environments.

Cheng-Yun Yang, Gowri Ramshankar, Nicholas Eliopoulos, Purvish Jajal, Sudarshan Nambiar, Evan Miller, Xun Zhang, Dave (Jing) Tian, Shuo-Han Chen, Chiy-Ferng Perng, Yung-Hsiang Lu

Proceedings of the 29th ACM/IEEE International Symposium on Low Power Electronics and Design, 2024

An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions.

Caleb Tung, Nicholas Eliopoulos, Purvish Jajal, Gowri Ramshankar, Cheng-Yun Yang, Nicholas Synovic, Xuecen Zhang, Vipin Chaudhary, George K. Thiruvathukal, Yung-Hsiang Lu

Proceedings of the 29th Asia and South Pacific Design Automation Conference, 2024

2023

An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions.

Caleb Tung, Nicholas Eliopoulos, Purvish Jajal, Gowri Ramshankar, Chen-Yun Yang, Nicholas Synovic, Xuecen Zhang, Vipin Chaudhary, George K. Thiruvathukal, Yung-Hsiang Lu

CoRR, 2023

Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem.

Purvish Jajal, Wenxin Jiang, Arav Tewari, Joseph Woo, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis

CoRR, 2023

PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages.

Wenxin Jiang, Nicholas Synovic, Purvish Jajal, Taylor R. Schorlemmer, Arav Tewari, Bhavesh Pareek, George K. Thiruvathukal, James C. Davis

Proceedings of the 20th IEEE/ACM International Conference on Mining Software Repositories, 2023

Reusing Deep Learning Models: Challenges and Directions in Software Engineering.

James C. Davis, Purvish Jajal, Wenxin Jiang, Taylor R. Schorlemmer, Nicholas Synovic, George K. Thiruvathukal

Proceedings of the IEEE John Vincent Atanasoff International Symposium on Modern Computing, 2023