MORPH reformulates ZKP MSM and NTT kernels into GEMM operations for TPUs using a new Big-T complexity model, achieving up to 10x NTT throughput over GZKP.
Canonical reference
Symp.High-Perform.Comput.Archit.(HPCA),2025,pp.409–422
Canonical reference. 75% of citing Pith papers cite this work as background.
34
Pith papers citing it
Background
75% of classified citations
citation-role summary
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method 1
citation-polarity summary
representative citing papers
Bifrost achieves significant latency reductions in privacy-preserving transformer inference through a hybrid CPU TEE and accelerator FHE design, with Bifrost+ further optimizing via prefill/decode split.
Conversation-level scheduling in ConServe observes first-turn input length and KV occupancy to route prefill once and pin decoders, cutting p95 time-to-first-effective-token by 51% and improving energy efficiency by 7.5% versus per-turn prediction baselines.
ITHICA generates functional tests via intra-thread instruction duplication and comparison, detecting 39% more defective servers than baseline methods on over 3000 real CPUs while revealing new defect behaviors.
IP-CaT jointly optimizes TLB and cache management for L1I prefetching via a translation prefetch buffer and trimodal replacement policy, yielding 8.7% geomean speedup over EPI across 105 server workloads.
Ocean uses HyperLogLog estimators to skip the costly symbolic phase of GPU SpGEMM, pairs it with dynamic workflow choice and a shared-plus-global hash accumulator, and reports 1.4-2.8x speedups over prior GPU implementations.
KOVAL-Q uses SAT solving to optimize and verify surface-code logical operations with general encodings, finding d-cycle CNOTs and 2d-cycle rotations that reduce FTQC application runtime by about 10 percent.
FlexiFlow optimizes carbon footprint for item-level intelligence on flexible electronics by modeling lifetime variation, delivering 1.62X microarchitectural and 14.5X algorithmic reductions plus a 30.9 kHz tape-out.
GreenCache dynamically manages LLM KV cache resources to reduce carbon emissions by 15.1% on average (up to 25.3%) while meeting latency constraints for over 90% of requests on real traces.
DiLaServe improves SLO attainment for diffusion language models by up to 56.6 percentage points and reduces latency by up to 46% with less than 1% accuracy drop via deadline-aware scheduling and dynamic reconfiguration.
HERALD enables near-lossless accuracy at 5-10% KV budget for block dLLMs by amortizing top-k selection across denoising steps and overlapping CPU-GPU retrieval, yielding up to 2.47x higher throughput than GPU-only inference.
A graph-theoretic nonlinear integer program solved via genetic algorithm reduces qubit transfers in neutral atom quantum circuit compilation compared to prior zoned-architecture compilers.
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.
OptCC is a pipelined AllReduce algorithm that completes within 2-6% of fault-free NCCL performance under up to 50% bandwidth loss by approaching a new lower bound showing O(1/p) unavoidable overhead for p GPUs.
Develops a simulation framework showing multi-resource stranding changes deployable capacity and effective costs in AI datacenters, arguing the key metric is deployable capacity over time rather than installed megawatts.
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.
EdgeFlow reduces mobile LLM cold-start latency up to 4.07x versus llama.cpp, MNN, and llm.npu by NPU-aware adaptive quantization, SIMD-friendly packing, and synergistic granular CPU-NPU pipelining at comparable accuracy.
Execution-idle accounts for 19.7% of GPU execution time and 10.7% of energy in a large cluster, motivating power management that treats it as a distinct operating state.
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.
PICO is a benchmarking framework for collective operations that decouples portable setup from platform execution, supplies reference MPI implementations, and shows default choices can be up to 5x slower with up to 44% end-to-end training time reductions in simulator replays.
AI data center electricity demand will reach 1% of global power use by 2030, with concentrated siting causing high power stress in specific regions like Oregon, Virginia, and Ireland.
DualScale reduces energy by up to 39% in prefill and 48% in decode for disaggregated LLM serving while meeting TTFT and TPOT SLOs on a 16x H100 cluster.
PRISM introduces a probabilistic performance modeling framework that quantifies guarantees on training time for large-scale distributed systems under runtime variability.
RAP is a reinforcement learning framework for runtime-adaptive pruning of LLMs that jointly optimizes model weights and KV-cache usage under varying memory budgets.