Paper

• MegaScale-Infer - MegaScale-Infer：MoE disaggregated attention/FFN serving，ping-pong pipeline + M2N 通信库，1.90× 吞吐提升
• Resilient AI Supercomputer Networking using MRC and SRv6 - MRC+SRv6+multi-plane Clos：三管齐下的 100K+ GPU AI 训练网络容错方案，OpenAI/Microsoft 生产验证

Summary

• A Lightweight High-Throughput Collective-Capable NoC for Large-Scale ML Accelerators - FlooNoC 扩展：multicast/归约/barrier + DCA 借 Snitch FPU；router +16.9% 面积，4×4 mesh 上 multicast 5.3×、reduction 2.8×，SUMMA GEMM 最高 3.8×
• A Preliminary Architecture for a Basic Data-Flow Processor - Dennis & Misunas (ISCA 1975) 基本数据流处理器：decider/T-gate/merge 条件迭代、Decision Units、Instruction Cell 两级存储作活跃指令 cache
• AI Accelerators for Large Language Model Inference: Architecture Analysis and Scaling Strategies - 首篇 LLM 推理加速器跨架构定量横评：五类（GPU/Systolic/SRAM-centric/WSE/Deterministic pipeline）六大操作域评估；expert parallelism 8.4× 参数-计算比但 2.1× 延迟方差
• Constable: Safely Eliminating Load Instruction Execution - ISCA 2024 Best Paper：likely-stable load 识别 + RMT/AMT 监控；12.4 KB/core；+5.1% perf、-3.4% 动态功耗、SMT +8.8%；与 EVES LVP 正交至 8.5%
• DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation - DeepSeek 半自回归 speculative decoding + 负载感知 confidence verify；离线 τ +16–31%，V4 生产 per-user +57–85% vs MTP-1，开源 DeepSpec
• Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks - MIT 65nm 168-PE CNN 加速器：Row Stationary dataflow、四级存储、GIN 组播 NoC、RLC+data gating；AlexNet 35 frames/s @278mW、0.0029 DRAM access/MAC
• FEATHER: A Reconfigurable Accelerator with Data Reordering Support for Low-Cost On-Chip Dataflow Switching - NEST+BIRRD 可重构加速器，RIR 在归约中做 arbitrary layout reorder；Layoutloop dataflow-layout 联合搜索，ResNet-50 1.27–2.89× 延迟、FPGA 2.65–3.91× 吞吐
• FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning - IO-aware 精确 attention 第二代：减 non-matmul、seq 维并行、warp split-Q；相对 FlashAttention ~2×，A100 73% 峰值 TFLOPs/s、GPT 训练 225 TFLOPs/s
• FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision - Hopper H100：TMA/WGMMA producer-consumer、2-stage GEMM-softmax 流水线、FP8 block quant+incoherent processing；FP16 740 TFLOPs/s、相对 FA2 1.5–2×
• FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness - IO-aware tiling + online softmax + 反向重算；O(N) 内存、IO-optimal HBM 访问；GPT-2 attention 7.6×、BERT 15% 快于 MLPerf 纪录、Path-X 16K 61.4%
• FlashDecoding++: Faster Large Language Model Inference on GPUs - 异步 unified-max softmax + M=8 flat GEMM 双缓冲 + FastGEMV/CUTLASS 启发式 dataflow；decode 相对 HF 最高 4.86×、FlashDecoding 平均 1.37×，NVIDIA/AMD 双平台
• FlashMoE: Fast Distributed MoE in a Single Kernel - NeurIPS 2025：单 persistent GPU kernel 融合 MoE 计算与 NVSHMEM RDMA；actor 调度 + payload-efficient dispatch；8×H100 最高 6× 延迟、5.7× 吞吐、93% SM（FP32 vs FP16 基线）
• Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective - LLM 推理加速硬件最完整综述：HBM-assisted vs SRAM-based 双路线、Quantization/Sparse/Speculative/Paged 关键专题、典型 FPGA/SoC 数据点
• LoopLynx: A Scalable Dataflow Architecture for Efficient LLM Inference - FPGA hybrid spatial-temporal dataflow：Macro Dataflow Kernels + state-machine 调度 + multi-FPGA ring；解决”spatial dataflow 在 decode 串行依赖下利用率不足”；双节点 1.67× A100、四节点 2.52× A100
• Near-Optimal Wafer-Scale Reduce - WSE Reduce/AllReduce 首次系统研究：性能模型（<4% 误差）、5 种算法（Auto-Gen ≤1.4× 下界）、3.27× 快于 vendor
• Optimization of Collective Reduction Operations - Rabenseifner ICCS 2004：MPI Reduce/AllReduce 五算法（tree、doubling、RHD、binary blocks、ring）与 (p,n) 选择；占 MPI 时间 >40%；长向量相对 vendor 最高 100×
• Plasticine: A Reconfigurable Architecture For Parallel Patterns - Stanford CGRA 直接支持 Map/FlatMap/Fold/HashReduce；64 PCU+64 PMU @28nm 112.8mm²、12.3 TFLOPS；相对 Stratix V 最高 76.9× Perf/W、DHDL 数分钟编译
• SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts - SN40L RDU（TSMC 5nm, 1040 PCU+PMU, 638 BF16 TFLOPS, 520 MiB SRAM+64 GiB HBM+1.5 TiB DDR）+ Samba-CoE（150 个 8B expert, 1T 总参）；streaming dataflow 编译期融合数百 op；vs DGX H100 3.7× speedup
• SpaDA: A Spatial Dataflow Architecture Programming Language - 空间数据流语言 place/dataflow/compute + GT4Py→CSL 优化编译；WSE-2 14× 减码、collective 1.04× 手写 CSL、260 TFlop/s stencil、82× GEMV vs A100
• TileLoom: Automatic Dataflow Planning for Tile-Based Languages - MLIR 编译框架：Triton/Helion → spatiotemporal dataflow planning + df 硬件模型；Tenstorrent Wormhole/Blackhole 上 FlashAttention ~2× TTNN、Mamba Scan 最高 27× unfused
• Understanding Inference Scaling for LLMs - Reasoning-centric LLM 推理系统瓶颈分析：Capacity Trap, Reasoning Cliff, DP→TP Transition, Prefill-Decode Divergence（8B-671B H200 实测）
• Voxel: 3D-Stacked AI Chip Efficiency for LLM Inference - Voxel 编译器感知 3D AI 芯片仿真框架：LLM prefill/decode 软硬件协同探索，Graphcore IPU 验证误差 ≤6.8%
• WaferLLM: Large Language Model Inference at Wafer Scale - 首个晶圆级 LLM 推理系统：PLMR 设备模型 + MeshGEMM/MeshGEMV + KV shift；WSE-2 上 E2E 10–20× SGLang/A100 集群、MeshGEMV 606× 单 A100