Paper proposes an algorithm to minimize long-term latency in task offloading.
problem Minimizing long-term latency in task offloading.
method Formulated a stochastic programming problem and proposed an algorithm for non-stationary bandit model.
result Proved asymptotic optimality of the proposed algorithm in a non-stationary fog-enabled network.
Deep model predicts robot trajectories in real time.
problem Real-time robot trajectory prediction with low latency.
method Deep conditional generative model trained with SGDVB.
result More accurate long-term predictions with lower latency.
PoDiRe learns long-term rewards in multi-task recommendations.
problem Long-term rewards in multiple recommendation tasks.
method Policy Distilled Reinforcement Learning (PoDiRe) combining deep reinforcement learning and knowledge distillation.
result PoDiRe outperforms state-of-the-art methods in real-world data.
A blockchain-based federated learning system with latency analysis.
problem Latency issues in decentralized federated learning architectures.
method Introduced a consortium blockchain and a latency model to analyze the workflow.
result The latency model accurately quantifies actual delays in the system.
Optimizes latency and false alarm probability in change detection problems.
problem Balancing latency and false alarms in non-stationary environments.
method Develops order-optimal change detectors under specified latency and false alarm levels.
result Derives a universal lower bound on latency and develops order-optimal detectors.
Convolutional neural network detects early seizures with low power microcontroller.
problem Early detection of seizures for patients with severe epilepsy.
method Energy-efficient convolutional neural network designed for implantable microcontrollers.
result Outperforms other detectors with high sensitivity and low false detection rate.
Study validates low latency's impact on trading profits.
problem Determining the impact of low latency on trading profits.
method Agent-based simulation of trading strategies in a controlled environment.
result Latency inversely affects trading profits; latency rank is key.
The paper analyzes optimal market making in latency-prone markets.
problem Optimal market making for large-tick assets in the presence of latency.
method Random walk model for asset price, Markov Decision Processes (MDP) formulation, explicit criteria for profitability.
result Market makers can earn positive expected profit under certain conditions.
Research examines how strategic latency manipulation impacts Ethereum's network efficiency and decentralization.
problem Impact of artificial latency on Ethereum's network efficiency and decentralization.
method Comprehensive analysis of MEV-Boost auction system and empirical validation with a pilot.
result Increased profitability for node operators and significant systemic challenges like heightened network inefficiencies and centralization risks.
The Tick library simulates and learns Hawkes processes with latency effects.
problem Correctly modeling causality in order book events with latency.
method Exponential kernels shifted by latency, derived log-likelihood expressions.
result Latency determines most decays in real data, showing decay relationships.
Research optimizes C++ patterns for HFT, reducing latency and improving profitability.
problem Optimizing latency-critical code for high-frequency trading systems.
method Creation of a Low-Latency Programming Repository, optimisation of trading strategy, implementation of Disruptor pattern.
result Significant performance improvements in speed and profitability.
MASnet enhances speech on mobile devices with low latency.
problem Efficiently enhancing speech on mobile devices with low latency.
method MASnet processes linear-scale spectrograms, using ratio masks to enhance noisy frames, and operates in low-latency incremental inference mode.
result MASnet achieves efficient speech enhancement with low latency, reducing FMA/s operations.
CryptoNAS improves PI accuracy by 3.4% with 2.4x less latency.
problem Private inference on machine learning models with limited latency.
method Developed CryptoNAS, a novel NAS method for finding models that maximize accuracy within a ReLU budget.
result Improves accuracy by 3.4% and latency by 2.4x over state-of-the-art methods.
Proposes deep-RL with GANs for ultra-reliable low-latency communication.
problem Resource allocation for URLLC with high reliability and low latency.
method Experienced deep-RL framework using GANs to pre-train and optimize resource allocation.
result Deep-RL framework achieves near-optimal reliability and latency under URLLC constraints.
PHAZE framework uses zkML and hashing for fast, verifiable LHC trigger decisions.
problem Inefficient inference on large machine learning models for LHC trigger performance.
method Cryptographic techniques like hashing and zkML for low latency, certifiable inference.
result Achieves nanosecond-order latency for LHC triggers, enabling dynamic low-level triggers.
Two solutions improve privacy-preserving inference with reduced latency and wider neural network support.
problem Balancing accuracy, security, and computational complexity in machine learning with sensitive data.
method Combining Homomorphic Encryption with transfer learning and novel data representation methods.
result More than 10x improvement in latency with wider neural network support.
Collage-CNN reduces cloud inference latency by 1.47X with 9X reduced latency variation.
problem Reducing latency variance in cloud machine learning inference.
method Proposes a novel Collage-CNN model that combines multiple images for classification, providing redundancy and cost efficiency.
result Significant reduction in 99th percentile tail latency and variation in inference latency.
The possibility of latency arbitrage in financial markets has led to the deployment of high-speed communication links between distant financial centers. These links are noisy and so there is a need for coding. In this paper, we develop a gametheoretic model of trading behavior where two traders compete to capture laten…
Automated tool reduces FPGA inference latency to 5 μs for deep neural networks.
problem Deploying ultra low-latency, low-power deep neural networks on FPGAs.
method Extending hls4ml library, using model compression techniques like pruning and quantization-aware training.
result Achieved inference latency of 5 μs with 97% resource reduction.
New strategy improves liquidity takers' performance in markets with latency.
problem Latency affects liquidity takers' ability to execute limit orders effectively.
method Modelled LOB and MLOs as a marked point process, used variational analysis and FBSDEs to find optimal price limits.
result Optimal trading strategy improves marksmanship in markets with latency.
Collage inference uses redundancy to reduce cloud image classification latency variance.
problem Reducing latency variance in cloud image classification.
method Integrates collage-cnn for low-cost redundancy in multi-image classification.
result Significant reduction in 99th percentile tail latency and inference latency variation.
TinyML models detect RF and cyber threats in spacecraft with low latency.
problem Detecting cyber-RF threats in autonomous spacecraft with low latency.
method Analysis of classical models (RF, LR, SVM, MLP) for latency-accuracy trade-offs.
result Logistic Regression achieves microsecond-level inference with minimal accuracy loss.
Speed bumps reduce but do not fully eliminate investment in fast trading technology.
problem Limiting low-latency trading to curb investment in fast trading technology.
method Built an experimental trading platform to test the effects of speed bumps on investment in fast trading technology.
result Asymmetric speed bumps reduce investment in speed by only 20%, and increasing the magnitude further reduces investment by 8.33%. Symmetric speed bumps have no effect on investment levels.
The paper explores how latency affects limit order placement and adverse selection risk.
problem Limit order placement and adverse selection risk.
method Stochastic control framework to exploit liquidity imbalance and measure latency impact.
result The added value of exploiting liquidity imbalance is reduced by latency.
Blockchain trading faces limits due to time-consuming settlement, exposing arbitrageurs to price risk.
problem Time-consuming settlement in blockchain trading limits arbitrage opportunities.
method Analysis of Bitcoin network and order book data.
result Cross-exchange price differences coincide with high settlement latency and low default risk.
A new algorithm for cryo-EM data collection that balances reward and latency.
problem Optimizing data collection in cryo-EM experiments with action delays.
method Latency-aware contextual bandit framework and COAF algorithm.
result The COAF algorithm efficiently maximizes reward over time in cryo-EM experiments.
High frequency trading has led to widespread efforts to reduce information propagation delays between physically distant exchanges. Using relativistically correct millisecond-resolution tick data, we document a 3-millisecond decrease in one-way communication time between the Chicago and New York areas that has occurred…
S3NAS finds high-accuracy CNN architectures for NPUs in 3 hours.
problem Finding optimal CNN architectures for NPUs with high accuracy and low latency.
method Supernet design, Single-Path NAS, scaling, analytical latency estimation.
result 82.72% top-1 accuracy on ImageNet with 11.66 ms latency.
DCT-SNN uses DCT to reduce inference latency in SNNs.
problem High inference latency in SNNs.
method Proposes a time-based encoding scheme using DCT to reduce timesteps.
result Achieves top-1 accuracy comparable to standard deep learning while reducing inference latency.
New algorithms decode Markov chains with near-optimal performance, even with small latency.
problem Online decoding of nth order ergodic Markov chains with latency constraints. method Deterministic and randomized algorithms using dynamic programs, with lower bounds established.
result Near-optimal performance of algorithms with minimal latency, outperforming existing methods.
PolyLUT uses polynomials to reduce FPGA latency.
problem Reducing latency in FPGA-based neural network inference.
method Training neural networks using multivariate polynomials as basic building blocks.
result Achieved significant latency and area improvements.
This study analyzes satellite communication latency using a stochastic geometry model.
problem Latency analysis of LEO satellite relay communication systems.
method Stochastic geometry framework with spherical BPP models, suboptimal satellite relay selection strategy.
result Derives distance distributions and analytical expressions for transmission delays.
Paper analyzes how latency affects optimal order execution in markets.
problem Balancing profit from optimal order execution against execution risk.
method Stochastic optimal control problem with risk-averse trader.
result Closed-form approximations of fill probability, limit price, and latency.
Paper presents FPGA implementation for efficient recurrent neural networks.
problem Implementing recurrent neural networks on FPGAs for low latency.
method Developed hls4ml framework to implement LSTM and GRU layers.
result Demonstrated effective designs for both small and large models.
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.
This paper proposes an EM approach to reduce inference latency in NAR sequence generation.
problem High inference latency in NAR models due to multi-modality in sequence generation.
method A unified EM framework that jointly optimizes AR and NAR models, with iterative refinement.
result The proposed approach achieves competitive performance with existing NAR models and significantly reduces inference latency.
Hierarchical FL reduces latency in HCNs by sharing model updates.
problem Latency and privacy issues in federated learning across heterogeneous cellular networks.
method Hierarchical federated learning, gradient sparsification, periodic averaging.
result Significant reduction in communication latency without compromising model accuracy.
Improves text-to-speech speed by interleaving character reading and audio synthesis.
problem Latency in text-to-speech models limits their use in time-sensitive tasks.
method Reinforcement learning to train an agent to choose the order of character reading and audio synthesis.
result The proposed method successfully balances latency and audio quality.
Flexible DNNs reduce inference latency without significant accuracy loss.
problem High computational cost and latency of deploying deep neural networks.
method Flexible DNN ensemble processing technique that dynamically adapts between accuracy and runtime.
result Achieves large reduction in average inference latency with minimal accuracy drop.
Cloud-native simulator studies global trading latencies.
problem Understanding latencies in planetary-scale automated trading systems.
method Cloud-native, open-source simulator with distributed clients.
result Demonstrates latencies in real-world trading systems.
Freezing intermediate layers reduces DNN inference latency.
problem High latency in deep learning inference due to computationally expensive models.
method Introduce approximate caching at each intermediate layer to avoid running all layers for many inference requests.
result Reduces the number of effective layers by half for 91.58% of CIFAR-10 requests.
Data mining enhances a heuristic for the Minimum Latency Problem.
problem Finding optimal solutions for the Minimum Latency Problem efficiently.
method Combining GRASP with data mining to find frequent patterns in high-quality solutions.
result Improved solution quality and reduced computational time compared to existing methods.
NeuraLUT maps neural networks to lookup tables, reducing latency and improving expressivity.
problem Reducing latency in deep neural networks for FPGA accelerators.
method Mapping entire sub-networks to a single lookup table, introducing skip connections.
result Up to 4.3x lower latency for the same accuracy.
Paper predicts interference for better LA in URLLC.
problem Improving LA for URLLC with strict latency and reliability.
method Exploits time correlation of interference for prediction.
result Predicted interference improves LA for URLLC.
Prophet predicts device qualities for FL to reduce training latency.
problem Bad candidate-selection leads to large training and reporting latency in FL.
method Each device predicts its own training and reporting phases using LSTM. The algorithm is implemented with DRL.
result The proposed approach outperforms reactive algorithms in real-world experiments.
Power-SMC reduces inference latency for training-free LLM reasoning.
problem Training-free LLM reasoning with low latency.
method Power-SMC, a training-free Sequential Monte Carlo scheme targeting sequence-level power distribution.
result Power-SMC reduces inference latency from 16-28× to 1.4-3.3× over baseline decoding.
DIET-SNN optimizes SNNs for faster, lower-energy image classification.
problem High inference latency and inefficient input encoding in SNNs.
method End-to-end backpropagation to optimize membrane leak and firing threshold.
result Achieves top-1 accuracy of 69% on ImageNet with 5 timesteps and 12x less compute energy.
Kolmogorov-Arnold Networks enable ultrafast online learning with fixed-point quantization.
problem Efficient online learning for high-frequency systems with strict memory constraints.
method Fixed-point online training on FPGAs exploiting B-spline locality in KANs.
result Kolmogorov-Arnold Networks are more efficient and expressive than MLPs for low-latency tasks.