Tessera performs kernel-granularity disaggregation on heterogeneous GPUs, achieving up to 2.3x throughput and 1.6x cost efficiency gains for large model inference while generalizing beyond prior methods.
Canonical reference
Tender: Accelerating large language models via tensor decomposition and runtime requantization,
Canonical reference. 87% of citing Pith papers cite this work as background.
57
Pith papers citing it
Background
87% of classified citations
citation-role summary
background 13
baseline 1
dataset 1
citation-polarity summary
representative citing papers
TCM finds provably optimal DNN accelerator mappings by pruning the search space up to 32 orders of magnitude with a new dataplacement concept, delivering 1.2-6.5x better energy-delay-product in 17 seconds instead of hours.
HMA-Serve enables efficient cross-vendor disaggregated LLM serving on memory-heterogeneous accelerators via phase-wise quantization, compute-transfer pipelining, and deferred dequantization, delivering up to 3.2x goodput and 4.8x goodput-per-dollar.
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.
Introduces three linearizable GPU concurrent queues: an adapted wait-free queue using segments, a bounded lock-free queue with wave-batched paths, and a bounded wait-free queue using 64-bit CAS operations.
AVMP separates KV and SSM cache pools behind unified virtual addressing with failure-triggered migration, cutting OOM events 7.6% and raising throughput 1.83-13.3x on synthetic loads and 2.36x on ShareGPT traces.
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.
AtomTwin.jl is a physics-native Julia framework for simulating neutral-atom quantum processors, with a demonstration of logical Bell state preparation using four ytterbium-171 atoms in movable tweezers.
Fleet adds a Chiplet-task level to GPU task models, enabling per-chiplet scheduling and cooperative cache reuse in persistent megakernels, yielding 1.3-1.5x lower LLM decode latency and up to 37% less HBM traffic on AMD MI350 hardware.
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.
InfiniLoRA decouples LoRA execution from base-model inference and reports 3.05x higher request throughput plus 54% more adapters meeting strict latency SLOs.
NestPipe achieves up to 3.06x speedup and 94.07% scaling efficiency on 1,536 workers via dual-buffer inter-batch and frozen-window intra-batch pipelining that overlaps communication with computation.
Qurator jointly optimizes queue time and fidelity for hybrid quantum-classical workflows across providers using quantum-aware DAG scheduling and a unified logarithmic fidelity score, achieving 30-75% wait reduction at high load with bounded accuracy cost.
PrefixWall mitigates APC side channels in multi-tenant LLM systems via selective prefix isolation, delivering up to 70% higher cache reuse and 30% lower latency than full-isolation baselines.
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.
KernelSight-LM simulates token-level LLM inference to predict per-kernel latencies and end-to-end metrics (TTFT, TPOT, throughput) with 12.1% and 3.8% kernel errors in cross-generation and target-measured tiers.
Kamera stores a low-rank patch with each position-free KV chunk to restore cross-chunk conditioning lost in naive reuse, enabling cheap reordering, sliding windows, and recall across attention mechanisms.
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.
ANNS-AMP adapts distance-computation precision to vector-space regions via a lightweight cluster-level predictor and a bit-serial accelerator, delivering 163.76x/10.57x/2.06x average speedups and 1100x/39.41x/6.66x energy reductions versus CPU/GPU/custom baselines with <2.7% accuracy loss.
CRAM-ER combines spintronic computational RAM with CMOS adder trees and software fine-tuning to deliver near-lossless DNN accuracy at up to 100x lower latency than CPU/GPU baselines.
ThriftAttention recovers 89.1% of the FP16 quality gap versus pure FP4 attention by running only 5% of query-key blocks in FP16 on long-context benchmarks.
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.
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.
citing papers explorer
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FlexPipe: Adapting Dynamic LLM Serving Through Inflight Pipeline Refactoring in Fragmented Serverless Clusters
FlexPipe introduces runtime pipeline refactoring for LLMs to achieve higher resource efficiency and lower latency in serverless GPU clusters with fragmentation.
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Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
PLENA introduces a co-designed system with three optimization pathways for long-context agentic LLM inference, claiming up to 2.23x throughput over A100 and 4.04x energy efficiency.
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Performance Analysis and Optimization of 3D Generative Diffusion Models across GPU Architectures
Profiling of Med-DDPM shows cuDNN kernels dominate training; TF32 Tensor Core activation and 3D channels-last layout reduce SM cycles up to 100x and raise Tensor Core utilization on A100 without quality loss.
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Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots
A reference-augmented offline learning framework for 6-DOF tracking control of tendon-driven continuum robots achieves 50.9% lower average position error than non-augmented baselines.
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Computing In Spintronic Memory: A Thermal Perspective
Spintronic CiM shows uniform temperature that increases linearly with participating memory cells and decreases linearly with array size.
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Energy-Aware Computing in the Year 2026
The paper reviews energy-aware computing literature and constructs a taxonomy organized by hardware/software aspects, measurement, optimizations, scheduling, scaling, consolidation, federated learning, and cooling.
- PureMagic: A Dynamic Scheduler for Lattice Surgery