Papers

• AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflows (2026)
  Tejas Singh Anand et al.
  4.39
• SonicSampler: Unified Tile-Aware Kernels for LLM Sampling and Speculative Verification (2026)
  Pragaash Ponnusamy et al.
  4.39
• Pearl: Automatic Code Optimization Using Deep Reinforcement Learning (2025)
  Djamel Rassem Lamouri et al.
  2.87
• CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe (2026)
  Tara Saba et al.
  1.83
• Regression Language Models for Code (2025)
  Yash Akhauri et al.
  1.44
• Interpreting Performance Profiles with Deep Learning (2025)
  Zhuoran Liu
  1.39
• GroupTuner: Efficient Group-Aware Compiler Auto-Tuning (2025)
  Bingyu Gao et al.
  1.22
• BurTorch: Revisiting Training from First Principles by Coupling Autodiff, Math Optimization, and Systems (2025)
  Konstantin Burlachenko et al.
  1.11
• ACPO: AI-Enabled Compiler Framework (2023)
  Amir H. Ashouri et al.
  0.33
• Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers (2018)
  Riyadh Baghdadi et al.
  —
• MLGOPerf: An ML Guided Inliner to Optimize Performance (2022)
  Amir H. Ashouri et al.
  —
• AnICA: Analyzing Inconsistencies in Microarchitectural Code Analyzers (2022)
  Fabian Ritter et al.
  —
• Compiler Auto-tuning through Multiple Phase Learning (2023)
  Mingxuan Zhu et al.
  —
• LLVM Static Analysis for Program Characterization and Memory Reuse Profile Estimation (2023)
  Atanu Barai et al.
  —
• MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems (2024)
  Flavio Ponzina and Tajana Rosing
  —
• LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale (2024)
  Jaehong Cho et al.
  —