6 citations · 8 across the 4 of their papers we have counts for
4 papers
Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Accelerators
Sarbartha Banerjee, Shijia Wei, Prakash Ramrakhyani +1
Trusted execution environments (TEEs) for machine learning accelerators are indispensable in secure and efficient ML inference. Optimizing workloads through state-space exploration…
Sidecars on the Central Lane: Impact of Network Proxies on Microservices
Prateek Sahu, Lucy Zheng, Marco Bueso +3
Cloud applications are moving away from monolithic model towards loosely-coupled microservices designs. Service meshes are widely used for implementing microservices applications m…
Bandwidth Utilization Side-Channel on ML Inference Accelerators
Sarbartha Banerjee, Shijia Wei, Prakash Ramrakhyani +1
Accelerators used for machine learning (ML) inference provide great performance benefits over CPUs. Securing confidential model in inference against off-chip side-channel attacks i…
SESAME: Software defined Enclaves to Secure Inference Accelerators with Multi-tenant Execution
Sarbartha Banerjee, Prakash Ramrakhyani, Shijia Wei +1
Hardware-enclaves that target complex CPU designs compromise both security and performance. Programs have little control over micro-architecture, which leads to side-channel leaks,…