LENS predicts NPU LLM inference latency with 2.15% mean error by profiling each bucket with two E2E measurements and composing results to capture bucketing non-linearity.
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InProceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1(Rotterdam, Netherlands)(ASPLOS ’25)
14 Pith papers cite this work. Polarity classification is still indexing.
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HexAGenT reduces the SLO scale required for timely agentic LLM workflow completion by an average of 20.1% at 95% attainment and 33.0% at 99% attainment on heterogeneous A100/H100/H200 clusters.
ReaLB balances multimodal MoE inference loads by switching vision-heavy experts to lower FP4 precision per device rank, hiding the change in the dispatch phase to deliver 1.10-1.32x speedup with <1% accuracy degradation.
HybridGen achieves 1.41x-3.2x average speedups over six prior KV cache methods for LLM inference by using attention logit parallelism, a feedback-driven scheduler, and semantic-aware KV cache mapping.
A new abstract interpretation algorithm enables sound optimistic analysis of e-graphs during equality saturation, unifying it with non-destructive rewriting and improving precision on cyclic SSA programs.
WHET applies fine-grained coefficient-to-slot transforms, plaintext compression, and modulus raising plus lightweight hardware tweaks to FHE accelerators, delivering 1.38-8.74x per-area gains and sub-millisecond CKKS bootstrapping.
ACALSim is a new simulation framework with customizable threading, event-driven execution, and shared-memory model that reports over 14x speedup versus SST and enables simulation of large LLaMA models that SST cannot complete.
NasZip delivers up to 8.4x speedup over CPU baselines and 1.69x over prior NDP accelerators for ANNS by combining near-data processing with statistics-based PCA early exiting, dynamic-float encoding, and data-aware neighbor mapping.
Empirical comparison of equality saturation versus stochastic search on five benchmarks to evaluate if e-graphs are superior for rewrite-based optimization.
ELMoE-3D achieves 6.6x average speedup and 4.4x energy efficiency gain for MoE serving on 3D hardware by scaling expert and bit elasticity for elastic self-speculative decoding.
AEGIS reduces inter-GPU communication by up to 81.3% in self-attention and reaches 96.62% scaling efficiency with 3.86x speedup on four GPUs for 2048-token encrypted Transformer inference.
CCCL delivers 1.34-1.94x faster cross-node GPU collectives via CXL memory pooling than 200 Gbps InfiniBand RDMA, with 1.11x LLM training speedup and 2.75x hardware cost reduction.
EnergAIzer predicts module-level GPU utilization from structured kernel patterns and feeds it into a power model to estimate dynamic power with 8% error on Ampere GPUs and 7% on H100 forecasts.
Aquas delivers a holistic hardware-software co-optimization framework on MLIR that models memory interfaces with cache effects and uses an e-graph retargetable compiler, achieving up to 15.61x speedup with 14.5% area overhead across four domains.
citing papers explorer
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Optimism in Equality Saturation
A new abstract interpretation algorithm enables sound optimistic analysis of e-graphs during equality saturation, unifying it with non-destructive rewriting and improving precision on cyclic SSA programs.
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Rewrite System Showdown: Stochastic Search vs. EqSat
Empirical comparison of equality saturation versus stochastic search on five benchmarks to evaluate if e-graphs are superior for rewrite-based optimization.