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Found 9 papers, 5 papers with code
Monitoring and Adapting ML Models on Mobile Devices
ML models are increasingly being pushed to mobile devices, for low-latency inference and offline operation.
DPack: Efficiency-Oriented Privacy Budget Scheduling
This DP budget can be regarded as a new type of compute resource in workloads of multiple ML models training on user data.
A Tale of Two Models: Constructing Evasive Attacks on Edge Models
In this paper, we introduce a new evasive attack, DIVA, that exploits these differences in edge adaptation, by adding adversarial noise to input data that maximizes the output difference between the original and adapted model.
Treehouse: A Case For Carbon-Aware Datacenter Software
The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path.
Privacy Budget Scheduling
We describe PrivateKube, an extension to the popular Kubernetes datacenter orchestrator that adds privacy as a new type of resource to be managed alongside other traditional compute resources, such as CPU, GPU, and memory.
BPF for storage: an exokernel-inspired approach
The overhead of the kernel storage path accounts for half of the access latency for new NVMe storage devices.
Operating Systems Databases
Characterizing and Taming Model Instability Across Edge Devices
We characterize different sources for instability, and show that differences in compression formats and image signal processing account for significant instability in object classification models.
Cost-Aware Robust Tree Ensembles for Security Applications
There are various costs for attackers to manipulate the features of security classifiers.
Bandana: Using Non-volatile Memory for Storing Deep Learning Models
Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM.
