Search papers, labs, and topics across Lattice.
100 papers published across 3 labs.
Achieving classification accuracy comparable to digital models, this novel XL MIMO system leverages wave-domain learning to drastically reduce hardware complexity.
Achieving consensus in a stochastic broadcast model with more than two processes reveals new strategies to minimize error in unreliable communication environments.
Retry strategies can amplify failures, but Adaptive Retry Budgeting can keep success rates near optimal even under stress.
Achieving a balance of low complexities in data dispersal, storage, retrieval, and recovery could redefine efficiency standards in distributed storage systems.
Aggregate latency masks crucial insights into LLM inference efficiency, revealing that backend changes and quantization effects significantly influence performance metrics.
SecureDrive-FL withstands Man-in-the-Middle attacks without sacrificing accuracy, achieving a remarkable balance of privacy and performance in federated learning.
Achieving over 16 seconds of I/O-compute overlap, FoldPipe redefines efficiency in training molecular ML models on constrained resources.
Achieving classification accuracy comparable to digital models, this novel XL MIMO system leverages wave-domain learning to drastically reduce hardware complexity.
Learning task relationships from distributed data can lead to a dramatic performance boost in multitask learning, outperforming traditional non-cooperative approaches.
Client averaging may suppress leading biases, but a hidden second-order bias persists, challenging assumptions in distributed optimization.
Merging differentially private models can be dramatically improved by addressing geometric obstacles, leading to better performance without sacrificing privacy.
Achieving both optimal online communication and guaranteed output delivery, SLIDE revolutionizes the efficiency of secure multi-party computation with Shamir secret sharing.
The fingerprint-space capacity for 100BASE-TX devices is a staggering $2.96\times10^{10}$ distinguishable states, crucial for enhancing device authentication in IIoT.
Static information flow control techniques can't secure off-chain components in blockchain systems, exposing critical vulnerabilities.
Anomalous contamination in training data can silently compromise web anomaly detection, leading to backdoor failures that are hard to detect.
SysComb enables state-aware system call filtering without the need for intrusive modifications, significantly enhancing application security with minimal performance overhead.
Post-quantum signatures could redefine secure distributed signing, but existing methods are often overlooked in favor of pre-quantum solutions.
Quantum Cloud Guard enables SMEs to secure their data against future quantum threats without needing extensive cryptographic expertise or infrastructure.
Achieving just 1-3% overhead for confidential inference on Blackwell GPUs could redefine the viability of secure AI model training and deployment.
Small research software teams can dramatically improve their disaster resilience by implementing practical strategies and leveraging institutional support systems.
Storage limitations, not compute power, dictate the scalability of whole-slide image embedding extraction, reshaping our approach to handling large-scale medical data.
Vision generative AI models could revolutionize edge applications, but only if we rethink how they are designed in relation to hardware constraints from the outset.
IBLTs can now measure unknown data differences before decoding, eliminating wasted resources and enhancing efficiency in set reconciliation.
Achieving consensus in a stochastic broadcast model with more than two processes reveals new strategies to minimize error in unreliable communication environments.
Achieving up to 24.2% cost savings in multi-cloud API gateway deployment while meeting strict latency requirements could revolutionize cloud infrastructure optimization.
Distinct execution characteristics of CUs and DUs reveal that optimizing processor performance in Open RAN could dramatically improve resource efficiency.
Partitioning algorithms with similar entanglement costs can incur drastically different execution penalties, revealing hidden trade-offs that could reshape DQC compiler design.
Sintr safeguards BFT systems against Byzantine clients, ensuring database integrity with minimal performance trade-offs.
VPP slashes pipeline bubble ratios to near zero, boosting throughput in long-context LLM inference without sacrificing performance on shorter sequences.
SCAFFOLD's struggles in federated optimization stem from its inability to reliably estimate the global gradient at the Edge of Stability, revealing critical limitations in its design.
KOPE's ability to retain and leverage past optimization experiences leads to a 1.54x speedup in kernel optimization tasks compared to existing methods.
Federated learning can now predict QoS failures in wireless networks, achieving near-centralized performance while preserving user data privacy.
Selective transmission of task-relevant image latents can significantly reduce communication costs while maintaining high accuracy in edge AI applications.
Syn2Logic generates state-of-the-art neuromorphic hardware without a single line of HDL code, revolutionizing how neuroscientists can implement their models.
TOPAS slashes job completion times by up to 49.4% in multi-agent LLM serving by intelligently balancing prefix caching and request scheduling.
Conditional total correlation serves as the precise information cost of parallelism in adaptive sampling, revealing critical insights into the efficiency of decoding strategies.
Transitioning between tracking and sampling modes in Rowhammer defenses can introduce critical vulnerabilities, as shown by a dramatic reduction in Sigries' security.
GIFT achieves user data isolation in LLM serving with minimal overhead, ensuring privacy without compromising performance.
Real-time anomaly detection can mitigate the risk of catastrophic failures in DeFi stablecoins, achieving over 96% accuracy in identifying threats.
DDPG-CVX achieves up to 32 times higher throughput for energy-harvesting wireless devices, revolutionizing collaborative computing efficiency.
Achieving over 2x speedup on TRSM operations reveals untapped potential in GPU optimization for complex matrix computations.
A machine learning predictor in BOOSTEDSOSA slashes scheduling error by up to 71.88%, transforming how HPC systems handle job runtimes.
PRISM achieves efficient enclave isolation with only 15% overhead, outperforming traditional Intel SGX while enhancing memory safety and remote attestation capabilities.
Augmentation strategies can dramatically boost the XRP Ledger's resilience against consensus disruptions, achieving robustness with minimal edge additions.
MeMark embeds watermarks directly in neuron states, ensuring ownership evidence survives even after output head replacement or extensive model modifications.
SILK detects every instance of stream modification in functional attacks while limiting quality loss to less than 0.76 percentage points across CNNs and 0.17 perplexity in LLMs.
FRESCO achieves unprecedented temporal safety for CHERI processors, eliminating stack use-after-return vulnerabilities with minimal performance impact.
Identity and evidence inadequacies can lead to catastrophic failures in agent delegation systems, revealing that existing reliability primitives are fundamentally flawed.
The open-source satellite software ecosystem is not only growing in popularity but is also marked by a surprising diversity of goals and programming languages that could redefine development practices in the field.
Retry strategies can amplify failures, but Adaptive Retry Budgeting can keep success rates near optimal even under stress.
DNA storage could revolutionize data archiving, but it needs to drop in price by up to 99.99999999% to compete with existing technologies.
Slasher can dynamically adjust datacenter power usage, ensuring operational resilience while safeguarding workload performance during critical power events.
Spatially-aware messaging can now be seamlessly integrated into existing IoT systems without altering client implementations, unlocking new possibilities for location-dependent applications.
QEF-GT-AdamW achieves superior robustness and convergence in decentralized learning over unreliable wireless networks, setting a new standard for communication efficiency.
APC-RLNC boosts packet delivery by nearly 10% and cuts latency by up to 23% in decentralized wireless networks, even under challenging conditions.
RNN-guided load balancing slashes global workload imbalance from 11.3% to 3.5%, drastically improving simulation efficiency in complex multicellular growth models.
Achieving up to 8x memory savings and over 10x speedup in query performance, this framework revolutionizes how we handle data-intensive operations in distributed systems.
Leveraging network intelligence can transform WAN-based distributed training, achieving efficiency levels closer to colocated systems.
Achieving over 40% reductions in latency and link usage reveals that optimizing neural communication before routing can dramatically enhance performance in irregular workloads.
Redwood achieves a groundbreaking 3.4x performance-per-watt gain, demonstrating that AI can autonomously design and deploy cutting-edge hardware in record time.
M-HySMap slashes routed multicast hops by up to 41.1% while enabling rapid incremental updates for SNN mapping on mesh NoCs.
Maia 200 achieves unprecedented AI acceleration while slashing energy costs, redefining the landscape for high-performance computing.
Achieving rapid earthquake magnitude estimation with a single station, SeisMamba outperforms traditional models in both speed and accuracy, even in unseen regions.
FedV-KGQA enables multi-hop reasoning across disjoint knowledge graph silos without compromising data sovereignty, achieving near-centralized performance.
A new feature-major codebook layout accelerates self-organizing map training by up to 621x, enabling the largest reported atlas of 1.05 million neurons on a single GPU.
Achieving over five times improvement in Latency-Error-Energy metrics could redefine efficiency standards for edge-deployable virtual sensing systems.
Achieving 90% accuracy in optical card reading without manual intervention could redefine the capabilities of physical Turing Machines.
Simthesizer achieves up to 284.96x faster simulation speeds while maintaining a mere 2.51% throughput error, revolutionizing how we model LLM serving systems.
Formally-verified NoCs can now be generated effortlessly, eliminating the tedious verification process while ensuring strong liveness guarantees.
Achieving almost stable matching in constant rounds on general bipartite graphs with minimal shared randomness could revolutionize distributed algorithms in large-scale networks.
Achieving up to 100x performance improvements in datacenter replication, scarHW challenges the limits of traditional consensus protocols.
Achieving over 100 times faster tensor decomposition on a single GPU could revolutionize simulations of complex quantum systems.
LLMs can optimize database queries on GPUs, achieving over 2.5x speedup by leveraging advanced execution strategies and kernel fusion.
A federated model could revolutionize how medical centers share and enhance device knowledge, ensuring local control while fostering collaborative improvement.
Current hardware fuzzing techniques are falling short, with significant gaps in input generation and feedback mechanisms that could undermine verification reliability.
Achieving a balance of low complexities in data dispersal, storage, retrieval, and recovery could redefine efficiency standards in distributed storage systems.
Targeted mixed-precision strategies can cut CFD simulation time and energy costs by over a third without sacrificing accuracy.
ROS2 Connect achieves lower latency and higher stability for remote robotic operations, revolutionizing how distributed systems communicate over WANs.
FLINT transforms LLM inference by integrating high-bandwidth flash, overcoming memory constraints that limit model deployment and performance.
Reducing satellite training time by nearly 19% while slashing energy consumption by up to 88% could revolutionize satellite-based machine learning.
Achieving a 98.6% energy reduction in cloud removal for LEO satellites could revolutionize Earth observation capabilities.
Aggregate latency masks crucial insights into LLM inference efficiency, revealing that backend changes and quantization effects significantly influence performance metrics.
pigzpp achieves up to 16 times faster compression than Python's gzip while maintaining full compatibility with existing gzip standards.
A probabilistic framework reveals that synchronization overhead can negate the expected advantages of full-tree parallelism in distributed search systems.
Energy-aware strategies in O-RANs can cut energy consumption by over 5% while maintaining performance, challenging traditional deployment models.
Best-effort computing can achieve a 92% scaling efficiency in evolutionary models while gracefully handling hardware failures—transforming our approach to high-performance digital evolution.
Eliminating thermal-induced tuning stalls in optical interconnects can boost MoE model performance by up to 3.8x, unlocking new potential for large-scale AI systems.
Trustless document co-signing is achievable over a lossy optical channel without intermediaries, redefining secure mobile communications.
HMT slashes hashing costs by 2.4x compared to Ethereum's Merkle Patricia Trie while adapting dynamically to changing access patterns.
Turning BGP hijack filtering from altruism into a market-driven transaction could fundamentally change how networks protect themselves against malicious announcements.
Trust evaluations can now adaptively integrate multi-source evidence while quantifying uncertainty, leading to more reliable collaborator selection in distributed systems.
Achieving up to 5.35x faster data processing for multimodal AI datasets could revolutionize how researchers handle and access diverse data formats.
TailSieve achieves up to 2.59x speedup in LLM rollouts by intelligently routing long-tail requests, transforming how we handle high-concurrency decoding.
Regularization in federated learning can be significantly influenced by the choice of update masks, revealing a critical trade-off between generalization and training efficacy.
Achieving a faster convergence rate in federated multiobjective optimization could redefine how we approach complex, conflicting objectives in distributed learning environments.
FedCC enables clients to handle ambiguity in data, leading to a remarkable 67.3% accuracy even when facing severe label distribution skews.
SplitLite slashes communication costs in federated learning by up to 93.5% without sacrificing performance, revealing a hidden structure in model training data.
Quantum computers could compromise major cryptocurrencies, but practical migration strategies to post-quantum security are within reach.
NICWhisper reveals that electromagnetic emissions from network interface cards can effectively identify network threats, achieving over 80% accuracy without analyzing packet data.
Major gaps in firmware security practices could leave the TianoCore community vulnerable, but targeted improvements could significantly enhance UEFI firmware integrity.
VIPER slashes design-space exploration time from hours to under a minute while achieving less than 10% error in performance predictions for Processing-in-Memory systems.
A decentralized bidding system for LLM agents not only enhances efficiency but also reduces manipulation risks, outperforming traditional orchestration methods.
The shift from ad-hoc to standardized carbon accounting in climate modeling reveals significant discrepancies in energy and emissions reporting that could reshape climate research practices.
SxSSD allows trusted applications to dynamically define FTL policies while preserving the security isolation of traditional SSDs, striking a crucial balance between flexibility and safety.
Achieving lower latency in secure IoT data aggregation, Phi-PHE-BC redefines the performance landscape for homomorphic blockchain architectures.
