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Found 15 papers, 5 papers with code
Leveraging cache to enable SLU on tiny devices
Our idea is simple: let the device match new inputs against cached results, and only offload unmatched inputs to the cloud for full inference.
Efficient Deep Speech Understanding at the Edge
This paper aims to enhance SU performance on edge devices with limited resources.
Secure and Effective Data Appraisal for Machine Learning
Essential for an unfettered data market is the ability to discreetly select and evaluate training data before finalizing a transaction between the data owner and model owner.
Federated Few-Shot Learning for Mobile NLP
Such an inadequacy of data labels is known as a few-shot scenario; it becomes the key blocker for mobile NLP applications.
Towards Practical Few-shot Federated NLP
To address these challenges, we first introduce a data generator for federated few-shot learning tasks, which encompasses the quantity and skewness of scarce labeled data in a realistic setting.
Efficient NLP Model Finetuning via Multistage Data Filtering
To this end, we set to filter training examples in a streaming fashion, in tandem with training the target model.
STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining
Yet, the unprecedented size of an NLP model stresses both latency and memory, creating a tension between the two key resources of a mobile device.
FedAdapter: Efficient Federated Learning for Modern NLP
A key challenge is to properly configure the depth and width of adapters, to which the training speed and efficiency is highly sensitive.
Clique: Spatiotemporal Object Re-identification at the City Scale
Object re-identification (ReID) is a key application of city-scale cameras.
Approximate Query Service on Autonomous IoT Cameras
Elf is a runtime for an energy-constrained camera to continuously summarize video scenes as approximate object counts.
Databases
Video Analytics with Zero-streaming Cameras
For efficiency, we advocate for these cameras to be zero streaming: capturing videos to local storage and communicating with the cloud only when analytics is requested.
Let the Cloud Watch Over Your IoT File Systems
To safeguard such data on smart devices, we present a novel storage stack architecture that i) protects file data in a trusted execution environment (TEE); ii) outsources file system logic and metadata out of TEE; iii) running a metadata-only file system replica in the cloud for continuously verifying the on-device file system behaviors.
Cryptography and Security
StreamBox-HBM: Stream Analytics on High Bandwidth Hybrid Memory
It dynamically optimizes for both the high bandwidth and limited capacity of HBM, and the limited bandwidth and high capacity of standard DRAM.
Databases
A First Look at Deep Learning Apps on Smartphones
We are in the dawn of deep learning explosion for smartphones.
DeepCache: Principled Cache for Mobile Deep Vision
We present DeepCache, a principled cache design for deep learning inference in continuous mobile vision.
