When will a server fail catastrophically in an industrial datacenter? Is it possible to forecast these failures so preventive actions can be taken to increase the reliability of a datacenter? To answer these questions, we have studied what are probably the largest, publicly available datacenter traces, containing more …
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,932 papers · 148 categories
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22 results for “datacenter”
Edge filters reduce video data transmission to datacenters.
problem Strain on wide area network infrastructure due to video camera deployments.
method FilterForward system with lightweight edge filters and microclassifiers.
result Reduces bandwidth use by an order of magnitude.
Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular, has the potential to learn optimal network policies. However, RL suffers from instability and over-fitting, deficiencies which so far rend…
This work surveys attacks and defenses on edge neural networks.
problem Security challenges of edge neural networks due to their compute and memory intensity, data-independence, and privacy risks.
method Taxonomy of attacks and defenses on edge-deployed neural networks.
result New security considerations and approaches are needed for edge DNNs.
NEST optimizes deep learning training by placing devices efficiently across networks and memory.
problem Inefficient device placement in distributed deep learning leads to high communication and memory overhead.
method NEST uses network-, compute-, and memory-aware dynamic programming to optimize device placement.
result NEST achieves up to 2.43 times higher throughput and better memory efficiency.
New activation networks improve model efficiency and performance.
problem Creating hardware-efficient deep learning models.
method Restructurable Activation Networks (RANs) with RAN-explicit and RAN-implicit methods.
result RANs achieve state-of-the-art results with improved hardware efficiency.
In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intens…
Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks. As the algorithms become mature and efficient, more and more ML inference is moving out of datacenters/cloud and deployed on edge devices. This…
A new decentralized federated learning approach tackles network capacity challenges.
problem Efficiently utilizing network capacities between nodes in federated learning.
method Proposes a segmented gossip approach for decentralized federated learning.
result Demonstrates significant reduction in training time compared to centralized federated learning.
The paper aims to define a benchmark for deep learning recommendation models.
problem Insufficient benchmarking for deep learning recommendation models.
method Synthesizes modeling strategies, defines desirable characteristics, and summarizes advice from the MLPerf Recommendation Advisory Board.
result Defines an industry-relevant benchmark for deep learning recommendation models.
Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only b…
New algorithms improve federated learning accuracy and stability with real-world data.
problem Real-world data diversity and imbalance challenge federated learning.
method Developed new algorithms (FedVC, FedIR) to resample and reweight data.
result Significant improvements in accuracy and stability of federated learning.
Decentralized deep learning improves efficiency with arbitrary compression.
problem Limited network bandwidth hinders decentralized deep learning.
method Proposed Choco-SGD with arbitrary compression for non-convex functions.
result Choco-SGD achieves linear speedup and higher compression than previous methods.
This paper analyzes CNNs for malware detection in cloud IaaS.
problem Malware vulnerability in cloud IaaS environments.
method Analysis of Convolutional Neural Networks (CNNs) for online malware detection using process-level performance metrics.
result State-of-the-art DenseNets and ResNets effectively detect malware in online cloud systems.
The wide adoption of DNNs has given birth to unrelenting computing requirements, forcing datacenter operators to adopt domain-specific accelerators to train them. These accelerators typically employ densely packed full precision floating-point arithmetic to maximize performance per area. Ongoing research efforts seek t…
In an era of ubiquitous large-scale streaming data, the availability of data far exceeds the capacity of expert human analysts. In many settings, such data is either discarded or stored unprocessed in datacenters. This paper proposes a method of online data thinning, in which large-scale streaming datasets are winnowed…
New attacks exploit neural network energy and latency, increasing costs by 10-200x.
problem Energy and latency vulnerabilities in neural networks.
method Crafted sponge examples to maximize energy and latency.
result Adversaries can increase energy consumption by 10-200x and delay decisions.
PruneFL reduces FL training time on edge devices by pruning model size.
problem Limited computation and communication resources on edge devices in FL.
method Adaptive and distributed parameter pruning during FL process.
result Pruned model converges to similar accuracy as original model with reduced training time.
Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of datacenter networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, net…
This paper improves federated learning efficiency by adaptively sparsifying gradients.
problem Efficiently training machine learning models with geographically dispersed data.
method Adaptive gradient sparsification for non-i.i.d. local datasets, fairness-aware, online learning approach.
result Up to 40% improvement in model accuracy for a finite training time.
Federated Learning can benefit from Model Agnostic Meta Learning for personalized models.
problem Personalizing models for diverse devices in Federated Learning.
method Interpreted Federated Averaging as a meta learning algorithm and applied MAML principles.
result Personalized models trained with Federated Averaging are easier to fine-tune and achieve higher accuracy.
"How much energy is consumed for an inference made by a convolutional neural network (CNN)?" With the increased popularity of CNNs deployed on the wide-spectrum of platforms (from mobile devices to workstations), the answer to this question has drawn significant attention. From lengthening battery life of mobile device…