Privacy and security challenges in Machine Learning (ML) have become increasingly
severe, along with ML's pervasive development and the recent demonstration of large attack …

Many applications have been offloaded onto cloud environments to achieve higher agility,
access to more powerful computational resources, and obtain better infrastructure …

Trusted execution environments (TEEs) for machine learning accelerators are indispensable
in secure and efficient ML inference. Optimizing workloads through state-space exploration …

Modern data centers have grown beyond CPU nodes to provide domain-specific
accelerators such as GPUs and FPGAs to their customers. From a security standpoint, cloud …

Trusted execution environment (TEE) promises strong security guarantee with hardware
extensions for security-sensitive tasks. Due to its numerous benefits, TEE has gained …

In federated learning (FL), data providers jointly train a model without disclosing their
training data. Despite its privacy benefits, a malicious data provider can simply deviate from …

Cloud workloads have dominated generative AI based on large language models (LLM).
Specialized hardware accelerators, such as GPUs, NPUs, and TPUs, play a key role in AI …

Abstract Machine learning technologies have the potential to transform and revolutionize
various industries, such as drug discovery by finding new molecules, medical diagnosis by …

Machine learning accelerators such as the Tensor Processing Unit (TPU) are already being
deployed in the hybrid cloud, and we foresee such accelerators proliferating in the future. In …

High Performance Computing (HPC) benefits from different improvements during last
decades, specially in terms of hardware platforms to provide more processing power while …