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cs.LG2025
REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving
Annabelle Sujun Tang, Christopher Priebe, Rohan Mahapatra +2
While model serving has unlocked unprecedented capabilities, the high cost of serving large-scale models continues to be a significant barrier to widespread accessibility and rapid…
cs.LG2023★ 1 cited
An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators
Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng +8
Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a physical-design…