Search papers, labs, and topics across Lattice.
100 papers published across 6 labs.
Achieving 99.64% detection precision with zero false negatives, this framework redefines ransomware detection in shared storage environments.
Clustering clients based on local novelty signals can revolutionize federated learning by enabling efficient and autonomous collaboration without the need for extensive computational resources.
New estimators for the Sliced Wasserstein distance leverage cumulative distribution functions to enhance scalability and efficiency in large datasets and federated learning contexts.
CRF-based aggregation in federated learning significantly boosts model performance by accurately reflecting client reliability and interactions, especially under data heterogeneity.
A new DRO framework that tailors uncertainty modeling to the data-acquisition process significantly boosts robustness and interpretability in learned reconstructions.
Achieving 99.64% detection precision with zero false negatives, this framework redefines ransomware detection in shared storage environments.
Clustering clients based on local novelty signals can revolutionize federated learning by enabling efficient and autonomous collaboration without the need for extensive computational resources.
New estimators for the Sliced Wasserstein distance leverage cumulative distribution functions to enhance scalability and efficiency in large datasets and federated learning contexts.
CRF-based aggregation in federated learning significantly boosts model performance by accurately reflecting client reliability and interactions, especially under data heterogeneity.
A new DRO framework that tailors uncertainty modeling to the data-acquisition process significantly boosts robustness and interpretability in learned reconstructions.
Atompack achieves a staggering 96x improvement in read performance for atomistic ML datasets, revolutionizing how we handle training data efficiency.
ANTAP achieves near-zero vulnerability to description-based attacks, fundamentally transforming how agents are evaluated and routed in multi-agent systems.
The Data Centre serves as the true body of AI, embodying human desires while remaining devoid of its own, complicating our understanding of intelligence in the context of Capital.
Balancing survivability and security in secret storage could redefine how we approach network resilience against both degradation and attacks.
Achieving 89.2% accuracy in microservice decomposition, MicroAgent outperforms traditional methods by a striking 24.6%.
EcoVideo achieves up to 2.9x faster video generation in resource-constrained environments by intelligently orchestrating cloud-edge dynamics.
CSAR revolutionizes robotics software development by providing a robust framework that ensures dependency isolation and reproducibility in complex distributed environments.
COSM achieves a remarkable 2.8x improvement in PIM throughput while keeping CPU performance degradation under 2.0%.
Only a subset of design interactions in heterogeneous LLM inference are binding constraints, revealing critical insights for optimizing deployment strategies.
Achieving an 18% boost in energy-tardiness efficiency, SMART-MIG redefines GPU scheduling for energy-conscious machine learning applications.
Mega achieves a groundbreaking energy efficiency of 0.375 pJ/SOP, setting a new standard for convolutional spiking neural network accelerators.
Achieving a 20.58x reduction in search cost for optimal neural network configurations on FPGAs could revolutionize resource-efficient AI deployment at the edge.
Asynchronous pipeline parallelism can match synchronous training performance if the right optimizer is chosen, debunking myths about gradient delay instability.
Centralization in blockchains persists due to a lack of incentives for user collaboration, but a new framework could fundamentally shift this dynamic.
A single compromised communication edge can account for up to 75% of total attack success in multi-agent systems, revealing critical vulnerabilities that can be proactively mitigated.
Accessing larger datasets could cost significantly more than previously assumed, scaling with the fourth root of size rather than remaining constant.
Making decision criteria explicit can transform how enterprises navigate the complex landscape of software development choices.
Selective recomputation in dynamic task environments reduces the overhead of Silent Data Corruption protection to just 0.5% of overall running time.
HSAP achieves unprecedented efficiency in training large language models by eliminating attention cross-contamination while maintaining high parallelism.
SubEdge slashes latency by nearly half during subscriber mobility, ensuring uninterrupted AI service delivery in 6G networks.
Festina slashes energy consumption by up to 56% in serverless LLM serving while ensuring SLOs are met, revolutionizing energy optimization in cloud workloads.
LLMs can autonomously generate efficient checkpoint/restart code for complex scientific applications, rivaling human expertise in resilience engineering.
StreamGuard can reduce the impact of failures in real-time data streams by up to 6x with minimal overhead, revolutionizing resilience in scientific computing.
Spandana achieves strict SLO compliance while slashing costs by up to 44% through intelligent request steering between VMs and serverless functions.
HMA-Serve achieves 3.2x higher goodput and 4.8x better cost-efficiency by leveraging memory-heterogeneous accelerators, challenging the status quo of single-vendor LLM serving.
Budget-adaptive routing can outperform strong models while cutting per-frame latency by nearly 30%—a game changer for edge-cloud inference efficiency.
Data placement, not function scheduling, is the key determinant of performance in edge-cloud serverless architectures, with a heuristic that scales to over 10,000 nodes while keeping latency stable.
Relativistic linearizability can be rigorously established for key distributed algorithms, revealing deeper insights into their behavior under the constraints of relativity.
CAEE slashes inference latency in MoE models by intelligently pruning low-value experts, achieving efficiency gains without sacrificing accuracy.
CryoZip slashes syndrome transmission bandwidth by over 14,238x while achieving unprecedented energy savings in quantum error correction systems.
Achieving a 1.41x speedup in shallow neural network training demonstrates the transformative potential of memory-access optimizations in GPU implementations.
SpikON slashes training latency and energy use for spiking neural networks while boosting throughput by over 7x compared to conventional edge accelerators.
Authentication isn't a barrier in quantum networks; it's a nuanced requirement that can be met with existing quantum-secure schemes tailored to specific applications.
Smart objects can now select service providers based on reliability scores, not just functional suitability, transforming IoT security dynamics.
Stateless adaptation can boost video streaming QoE by up to 6x in decentralized networks, revolutionizing how we approach ABR in IPFS environments.
Unused AI computation at the edge can be harnessed to boost performance in traditional tasks without sacrificing the efficiency of primary workloads.
Tri-serve redefines energy efficiency in multimodal inference by addressing hidden power inefficiencies, achieving a 22% boost without latency trade-offs.
Fog computing could revolutionize LLM deployment by slashing latency and enhancing privacy, making AI more accessible and efficient.
NI-ORCA achieves up to 30x speedups in counting non-induced graphlet orbits, revolutionizing the efficiency of graph analytics.
KernelFlume slashes operational costs by up to 61% while maintaining low latency for long-context LLM decoding, revolutionizing how we scale model serving.
Achieving a 2-orders-of-magnitude energy efficiency improvement, the new AIS framework outperforms prior analog generative models by up to 4x.
DMuon slashes training time for large models, achieving up to 163x faster optimizer steps while maintaining the benefits of matrix-aware updates.
RolloutPipe slashes rollout-to-training time by up to 42% while keeping on-policy correctness intact, revolutionizing resource efficiency in LLM training.
Quantization is not just a compression technique; it fundamentally reshapes the performance and robustness of Federated Learning systems.
Over a million context-specific optimization pipelines can be synthesized to enhance order fulfillment efficiency in warehouses.
Achieving a 66.9x speedup in encrypted GNN inference opens the door to real-time privacy-preserving analytics on massive financial graphs.
ApproxHDC reveals that automated approximation tuning in Hyperdimensional Computing can yield significant performance boosts without sacrificing accuracy, challenging conventional deep learning paradigms.
Tilikum achieves 39x higher throughput while completely blocking state-of-the-art reordering attacks in DAG-based systems.
LLMs can effectively detect architectural anti-patterns in microservices, but they still fall short in scenarios requiring explicit structural insights.
SpaceRipple achieves substantial bandwidth savings while delivering mission-critical semantic information, transforming how satellite networks handle Earth observation data.
Xsim achieves less than 5% error in predicting training times for heterogeneous AI systems, revolutionizing how we simulate and optimize distributed LLM training.
Large GPU workloads can leave a lingering cache state that dramatically impacts CPU performance, but a simple recovery mechanism can alleviate this issue.
Heterogeneous CGRAs can boost energy efficiency, but a homogeneous design can deliver up to 5x faster execution for matrix computations with significantly lower area overhead.
Adaptive page-aware scheduling can boost LLM serving throughput by over 1.3x on commodity GPUs, challenging existing optimization paradigms.
DKVE slashes the need for traditional key transparency queries by two orders of magnitude, making secure messaging feasible for billion-user systems.
Random Subset and Closest Available Neighbor First techniques redefine group formation efficiency for Flying Light Specks, depending on group size.
Dynamic scheduling in SegFold unlocks nearly 2x speedup over traditional SpGEMM accelerators by optimizing data reuse and load balance.
CHAMB-GA scales genetic algorithms across distributed systems with minimal overhead, revolutionizing how we approach computationally intensive optimization tasks.
Moebius achieves up to 1.25x better performance in reinforcement learning rollouts by seamlessly switching between expert and tensor parallelism without disrupting in-flight requests.
BtrLog slashes commit latency and boosts transaction throughput, outperforming traditional cloud logging solutions like EBS.
Memristive architectures could slash energy usage in edge computing, outperforming traditional CMOS microcontrollers by integrating computation and storage.
FedReLa achieves significant accuracy gains for minority classes in federated learning without requiring knowledge of global class distributions.
Clipping advantage fluctuations can lead to stable convergence in cooperative multi-agent reinforcement learning, outperforming traditional methods.
Even the most secure privacy protocols can be compromised by user behavior, with 17.65% of Railgun withdrawals traceable to deposits.
Merging multiplication and addition in FPGA architectures can boost energy efficiency for image segmentation tasks by up to 9x compared to existing methods.
Achieving a staggering 99.98% reduction in data exchange, Commerge revolutionizes multi-robot coordination by ensuring efficient map merging with minimal communication overhead.
A dynamic load balancer can reduce node idle time to nearly a millisecond in complex UQ workflows, revolutionizing how we approach scheduling in high-performance computing.
EmuGEMM achieves up to 5.5x speedup over cuBLAS ZGEMM while maintaining accuracy, revolutionizing low-precision matrix multiplication on modern GPUs.
Optimizing deployment strategies on the M1 AMX can double throughput for LLM prefill operations, far exceeding existing libraries.
Serializability can be verified in polynomial time with a fixed bound on transaction order width, challenging traditional MVSG assumptions.
Dependency concentration in microservices mirrors a black hole, revealing critical insights into service interdependencies that could jeopardize system evolution.
NEMS technologies could revolutionize hardware security by offering a physically robust solution that is resilient to sophisticated attacks like tampering and counterfeiting.
Query optimizers in Confidential Virtual Machines can be recalibrated to recover nearly half of the performance lost due to outdated assumptions.
Persistent pipeline compilation state poses a significant privacy risk in WebGPU, revealing how browser and GPU interactions can leak sensitive information.
FHPLF reduces communication costs and enhances privacy without sacrificing model accuracy, outperforming traditional methods in real-world applications.
TL++ outperforms conventional federated learning methods by over 12 percentage points in accuracy while slashing communication costs by 13.1-fold.
Time-shifted clients can achieve significantly higher throughput with existing ABR algorithms, even during network congestion.
Achieving a staggering 220x speedup in MaxSim scoring while preserving exact retrieval quality could revolutionize the efficiency of multi-vector retrieval systems.
EP-NCO slashes service response times by up to 50% in edge-cloud systems, outpacing traditional optimization methods.
AWS emerges as the most economical choice for serverless functions, while Microsoft Azure's pricing models could deter cost-sensitive users.
A decentralized system can evolve without central control, but it requires a new approach to governance and funding that separates power from monetary influence.
A million p-bits in a single programmable architecture reveals a universal tradeoff between throughput and accuracy in distributed probabilistic computing.
GPUSparse achieves a staggering 235x speedup over traditional CPU methods while maintaining exact scoring, revolutionizing real-time retrieval efficiency.
Achieving optimal solutions for engineering design problems with a distributed quantum simulator that rivals classical methods in efficiency and accuracy.
FinWhale achieves two-message delay termination in DAG-based Byzantine consensus, a breakthrough that could redefine throughput limits in decentralized systems.
Static low-bit gradient communication can destabilize training, but NEURON-Fabric ensures accuracy is preserved while slashing communication costs.
Interference-aware service placements can drastically enhance response performance in cloud-native applications by explicitly controlling for cross-application resource competition.
SOLAR achieves zero observed violations in Speed-of-Light performance analysis, transforming how we approach deep learning optimizations.
Achieving up to 13.9x speedup in LLM inference by leveraging cache-resident execution could redefine performance benchmarks for large models on standard CPUs.
Achieving a thermal-load correlation of R^2 = 0.9911 could redefine performance optimization in advanced packaging technologies.
BluTrain outpaces PyTorch in both speed and memory efficiency, setting a new standard for AI training frameworks.
Advanced scheduling strategies can significantly enhance the resilience of batteryless IoT systems, but the best choice depends on energy storage capacity and workload unpredictability.
Unlearning in federated systems can now be achieved in seconds without sacrificing model integrity, thanks to a novel filter-based approach.
Decentralized training can rival centralized models, achieving competitive performance while democratizing access to AI development resources.
Long-tailed distributions in federated graph learning can be effectively tackled with a dual decoupling approach that boosts minority node performance without compromising majority class accuracy.
