Microsoft
Unified Accelerators for Post-Moore Machine Learning
The slowdown in Moore’s Law has pushed high-end GPUs towards narrow number formats to improve logic density. This introduces new challenges for accurate Deep Neural Network (DNN) training and inference. Our research aims to bring novel solutions to the challenges introduced by ubiquitous ever-growing DNN models and datasets. Our proposal…
Tyche: Confidential Computing on Yesterday’s Hardware
Confidential computing is an increasingly popular means to wider Cloud adoption. By offering confidential virtual machines and enclaves, Cloud service providers now host organizations, such as banks and hospitals, that abide by stringent legal requirement with regards to their client’s data confidentiality. Unfortunately, confidential computing solutions depend on bleeding-edge emerging…
Invariant Federated Learning: Decentralized Training of Robust Privacy-Preserving Models
As machine learning (ML) models are becoming more complex, there has been a growing interest in making use of decentrally generated data (e.g., from smartphones) and in pooling data from many actors. At the same time, however, privacy concerns about organizations collecting data have risen. As an additional challenge, decentrally…
TTL-MSR Taiming Tail-Latency for Microsecond-scale RPCs
We consider a web-scale application within a datacenter that comprises of hundreds of software components, deployed on thousands of servers. These versatile components communicate with each other via Remote Procedure Calls (RPCs) with the cost of an individual RPC service typically measured in microseconds. The end-user performance, availability and overall…