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Found 10 papers, 2 papers with code
SEASONS: Signal and Energy Aware Sensing on iNtermittent Systems
Both energy-aware, batteryless intermittent systems and signal-aware adaptive sampling algorithms (ASA) aim to maximize sensor data accuracy under energy constraints in edge devices.
CacheGen: KV Cache Compression and Streaming for Fast Language Model Serving
Compared to the recent systems that reuse the KV cache, CacheGen reduces the KV cache size by 3. 7-4. 3x and the total delay in fetching and processing contexts by 2. 7-3. 2x while having negligible impact on the LLM response quality in accuracy or perplexity.
Automatic and Efficient Customization of Neural Networks for ML Applications
ML APIs have greatly relieved application developers of the burden to design and train their own neural network models -- classifying objects in an image can now be as simple as one line of Python code to call an API.
Acela: Predictable Datacenter-level Maintenance Job Scheduling
While it is tempting to use prior machine learning techniques for predicting job duration, we find that the structure of the maintenance job scheduling problem creates a unique challenge.
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization
Such solutions monitor past system executions to learn the system's behavior under different hardware resource allocations before dynamically tuning resources to optimize the application execution.
Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression
Our evaluation shows that compared to the state-of-the-art SEML approach in computer systems optimization, Cello improves latency by 1. 19X for minimizing latency under a power constraint, and improves energy by 1. 18X for minimizing energy under a latency constraint.
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction
To predict stragglers accurately and early without labeled positive examples or assumptions on latency distributions, this paper presents NURD, a novel Negative-Unlabeled learning approach with Reweighting and Distribution-compensation that only trains on negative and unlabeled streaming data.
Orthogonalized SGD and Nested Architectures for Anytime Neural Networks
We propose a novel variant of SGD customized for training network architectures that support anytime behavior: such networks produce a series of increasingly accurate outputs over time.
QURATOR: Innovative Technologies for Content and Data Curation
In all domains and sectors, the demand for intelligent systems to support the processing and generation of digital content is rapidly increasing.
ALERT: Accurate Learning for Energy and Timeliness
An increasing number of software applications incorporate runtime Deep Neural Networks (DNNs) to process sensor data and return inference results to humans.
