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"mobile edge proxy와 protocol split 설계에 해당한다." 값의 "Applicability" 속성을 가진 모든 문서의 목록입니다. 결과가 얼마 안 되기 때문에 주변의 값을 표시합니다.

1번 부터의 결과 26개입니다.

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결과 목록

  • Lesson:research autopilot 20260723t030001z  + (alpha-factory: alpha-factory and similar e … alpha-factory: alpha-factory and similar evolutionary alpha-discovery pipelines with an eligibility/diversifier admission track gated on standalone fitness and correlation-to-elite. Bounded here to two universes from one Yahoo-adjusted ETF vendor snapshot and a seed-only pool (max_generations=0); an evolved pool or cross-vendor data could still admit a decorrelated fitness-positive crisis sleeve.correlated fitness-positive crisis sleeve.)
  • Lesson:research autopilot 20260721t150001z  + (azure_inference_queueing: Single-period LP … azure_inference_queueing: Single-period LP-vs-IP relaxation gap of a multidimensional (m-constraint) 0/1 knapsack with nonnegative values and weights; verified for m=2 (memory×compute) on CPU-synthetic instances. Does not extend to full-horizon/rolling optimality, arrival dynamics, real serving traces, or any Azure production/SLA claim, and does not certify any greedy policy.m, and does not certify any greedy policy.)
  • Lesson:research autopilot 20260722t210001z  + (complex-nn-signal: Synthetic single-tone L … complex-nn-signal: Synthetic single-tone LFM chirp classification, CPU, K=4/L=16, one optimizer, descriptive SESOI verdicts, not externally preregistered and no captured data. The full escape holds at SNR=0; the SNR=-5 case only partially closes. The adaptive-gate lesson generalizes to models with an optional/nuisance DOF and a data-scaled loss gradient, but the specific accuracy numbers and the exact nulling lambda are task- and SNR-specific.nulling lambda are task- and SNR-specific.)
  • Lesson:technical review rigorous rental memory management for embedded systems 1b4e69c5  + (contiguous device memory를 쓰는 embedded/mobile 시스템의 idle capacity 재활용에 해당한다.)
  • Lesson:rigorous rental memory management for embedded systems 5632faf0  + (contiguous device memory를 쓰는 embedded/mobile 시스템의 idle capacity 재활용에 해당한다.)
  • Lesson:dwkv doomed chase domino  + (deadline scheduling, overload control, best-effort admission, heuristic priority queues)
  • Lesson:technical review appwatch detecting kernel bug for protecting consumer electronics applications 779775f3  + (device-driver 신뢰성이 낮은 embedded product의 critical-process protection에 해당한다.)
  • Lesson:appwatch detecting kernel bug for protecting consumer electronics applications a27fb99c  + (device-driver 신뢰성이 낮은 embedded product의 critical-process protection에 해당한다.)
  • Lesson:technical review transparent and selective real time interrupt services for performance improvem b78f264b  + (embedded Linux의 real-time interrupt handling과 throughput 보호에 해당한다.)
  • Lesson:transparent and selective real time interrupt services for performance improvement 6b173779  + (embedded Linux의 real-time interrupt handling과 throughput 보호에 해당한다.)
  • Lesson:technical review daac device reserved memory as an eviction based file cache f0f149f0  + (embedded/mobile reserved-memory reclamation과 secondary file cache에 직접 해당한다.)
  • Lesson:daac device reserved memory as an eviction based file cache db4ed198  + (embedded/mobile reserved-memory reclamation과 secondary file cache에 직접 해당한다.)
  • Lesson:dwkv failed run fail closed  + (experiment runners, benchmark data quality, failure recovery, reproducibility audits)
  • Lesson:halfmoon log optimal fault tolerant stateful serverless computing e44c470a  + (fault-tolerant stateful FaaS. Limits: 외부 상태 의미론과 workload 분류, 프로토콜 전환 정확성에 의존한다.)
  • Lesson:the design and implementation of a capacity variant storage system 91c1817e  + (flash SSD와 로그 구조 파일시스템. Limits: 사용자 용량을 희생하며 custom SSD·FS·manager와 수명 가정이 필요하다.)
  • Lesson:technical review energy efficient scheduling of real time tasks on multicore processors cc0782cd  + (homogeneous multicore real-time scheduler의 energy-aware partitioning/core parking에 해당한다.)
  • Lesson:energy efficient scheduling of real time tasks on multicore processors a70b387a  + (homogeneous multicore real-time scheduler의 energy-aware partitioning/core parking에 해당한다.)
  • Lesson:technical review z journal scalable per core journaling 1f19a370  + (many-core ext4-like journaling filesystem과 낮거나 중간 수준 공유의 metadata/fsync workload에 높음.)
  • Lesson:z journal scalable per core journaling 2aea3506  + (many-core ext4-like journaling filesystem과 낮거나 중간 수준 공유의 metadata/fsync workload에 높음.)
  • Lesson:technical review catching two rabbits adaptive real time support for embedded linux 07b12680  + (mixed real-time/best-effort embedded Linux workload에 해당한다.)
  • Lesson:catching two rabbits adaptive real time support for embedded linux ee13b80e  + (mixed real-time/best-effort embedded Linux workload에 해당한다.)
  • Lesson:research autopilot 20260719t210001z  + (mlir-fft-compiler: Established for dense ( … mlir-fft-compiler: Established for dense (all-GENERAL entries) complex constant-matrix contractions lowered to fp64 real arithmetic on an FMA machine, one contraction per thread, under a roofline model with perfect compute/memory overlap, no spill traffic above the 255-register cap, no L2 reuse or ILP modeling, and a swept rather than measured occupancy-to-bandwidth saturation knee. The 100% agreement figure is internal consistency between a closed-form rule and an emit-and-measure oracle sharing that model — it does not establish that the model matches GPU hardware. Does not apply to kernels with trivial or root-of-unity entries (FFT twiddles, radix-≤8 DFT base cases), which get no instruction win at all, nor to tiled or shared-memory-staged implementations, whose register accounting differs at large input counts.</br>openevolve-moe-prototype: Established on LLM-driven evolutionary program search (OpenEvolve-style) over 5 working-harness tasks with ~20 iterations each: 99 parent-child transitions, 30 pairable families, 5 conversions. The clustering result pools all transitions and is the robust one (p ~ 1e-3); the family-level null and the per-family-size gradient are underpowered (1-19 families per cell) and should be treated as directional. Parent quality is normalized against each task's observed score range at small N, so high-quality parents are an upper bound on true saturation and the saturation arm is if anything overstated. The independent-sibling curve is a null baseline, not a forecast of what raising family width would deliver. The causal source of sibling clustering (sampler temperature and near-duplicate proposals versus a parent's local optimization basin) is measured but not yet separated, so the actionable 'raise family width plus proposal diversity' recommendation remains untested.</br>spectral-operator-compiler: Measured on CPU (torch 2.13.0+cu130, 16 threads, single machine) for the 1-D FNO spectral contraction "bim,iom->bom" over five representative shapes (batch 4-32, channels 32-512, modes 16-64). The specific 4-14x magnitude is backend-, shape- and thread-count-specific and should not be assumed to transfer to GPU, where launch costs and the compute:bandwidth balance differ; the layout-ownership regression in particular depends on the batch:parameter size ratio. The methodological lesson — measure achieved throughput and thread scaling before trusting an arithmetic ceiling — is backend-agnostic and applies to any operator where an algebraic rewrite is being considered, notably the Gauss/Karatsuba 3M lowering decisions in sibling compiler projects. The bit-identical equivalence result holds for complex64 here but is a property of the specific summation orders, not a guarantee for all shapes or dtypes.for all shapes or dtypes.)
  • Lesson:research autopilot 20260720t030001z  + (mlir-fft-compiler: Established for dense a … mlir-fft-compiler: Established for dense all-GENERAL complex128 contractions y = W*x with compile-time constant W, one thread per contraction, on NVIDIA L40S (sm_89, 255-register cap), comparing a four-FMA-chain real lowering against a Gauss 3-multiply lowering with hoisted input sums. The threshold-vs-knee conclusion and the excess-register reduction should generalize to other register-limited straight-line GPU kernels and other NVIDIA architectures sharing the 255-register cap, but the fitted timing coefficients are L40S- and precision-specific. Two boundaries are explicitly unestablished: only square shapes at a single reuse factor K were measured, so the reload law's K-dependence (rho*K vs constant) is unidentified and the models diverge ~4x off the diagonal; and the corollary that tiling J below the spill boundary recovers the amortization win at wide shapes is a prediction of the model, not a measured result.</br>multi-lora-fusion: Established on CPU (AMD Ryzen 9 9950X, 32 MiB usable L3), float32, single LoRA layer, single thread, torch.bmm, for the LoRA shape family X:(n,d), B:(d,r), A:(r,k). The containment argument is hardware- and kernel-independent and should transfer wherever the output tensor is counted inside the working set; the specific bound value is not, since it depends on knee/(usable_fraction*cache). On GPUs where the output tensor claims only a fraction of L2 (~0.5 measured on an L40S) the bound tightens and inversion becomes conceivable, requiring the working-set knee to exceed half the L2 -- untested. The 900-shape grid is analytic cap arithmetic; only two shapes were measured end-to-end. Cap VALUES do not transfer across shapes (knee re-fit at 14.8 MiB vs 22.7 MiB reference), so per-shape calibration is still required even though the ordering result holds universally.</br>spectral-operator-compiler: The closed form holds for any two optimizations that remove disjoint, additively-decomposable stages of a single operator's wall-clock, independent of backend or hardware; it degrades when the levers share a stage or when one lever changes the cost of another's stage. The specific measured speedups are bounded to FNO 1-D spectral convolution forward at inference (eval, no_grad), torch 2.13, CPU, 16 threads, at channel-heavy shapes (Cin >= 256) where both stage fractions are large. At small channels (Cin = 32/64, f_wp ~ 0.02) the cache lever is within run-to-run noise and the efficiency ratio is uninformative. No GPU measurement was performed.</br>openevolve-moe-prototype: Established on 5 working-harness tasks of one LoRA-expert OpenEvolve pilot (~20 candidates per task, 30 pairable families, median children-per-parent k = 2), so magnitudes are pilot-specific and the residual near-miss effect is underpowered — the direction is what holds, not the size. The methodology (matched non-sibling controls, layer-wise covariate stripping, within-cell label permutation, per-channel decomposition, covariate-conditioned iid baselines) generalizes to any tree-structured search where preference pairs are mined from parent-child transitions. One boundary condition is explicit: similarity here is textual (difflib over tokens), so the sampler hypothesis is only ruled out for textual duplication — a sampler emitting semantically equivalent but textually distinct siblings would be scored as diverse and would require an AST-level or semantic proxy to exclude. or semantic proxy to exclude.)
  • Lesson:research autopilot 20260720t030001z-gpu  + (mlir-fft-compiler: Only the recorded shapes, dtypes, software revision, and physical L40S GPU 3. multi-lora-fusion: Only the recorded shapes, dtypes, software revision, and physical L40S GPU 3.)
  • Lesson:research autopilot 20260719t210001z-gpu  + (mlir-fft-compiler: Only the recorded shapes, dtypes, software revision, and physical L40S GPU 3.)
  • Lesson:research autopilot 20260721t030001z  + (mlir-fft-compiler: Pre-registered held-out … mlir-fft-compiler: Pre-registered held-out validation of analytical GPU/compiler cost models where a rate parameter is under-identified by available calibration data; strongest when a hardware performance counter is a linear function of the parameter. Bounds: establishes experiment resolving power and design adequacy, not which law is true; assumes the candidate set is frozen and cannot exclude unmodeled functional forms consistent with the calibration point.rms consistent with the calibration point.)
원본 주소 "https://s3wiki.yonsei.ac.kr/index.php/특수:속성별검색/:Applicability/mobile-20edge-20proxy와-20protocol-20split-20설계에-20해당한다."