5 papers
Coco: An Agentic Copilot for the Hardware--Software Co-Design Lifecycle
Samuel Kushnir, Kavya Sreedhar, Yeshwanth Reddy Pogula +8
Co-designing ML models and the accelerators that run them is an unusual reasoning task: architects must draw confident, high-stakes conclusions about systems that do not yet exist,…
Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool
Samuel Kushnir, Kimia Noorbakhsh, Kavya Sreedhar +6
Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions baked into today's abstractions are invalidated by tomorrow's models an…
OPTIMA: Optimal One-shot Pruning for LLMs via Quadratic Programming Reconstruction
Mohammad Mozaffari, Samuel Kushnir, Maryam Mehri Dehnavi +1
Post-training pruning cuts the inference cost of large language models, yet it faces a trade-off: simple heuristics that zero weights are fast but degrade accuracy, while principle…
CafeQ: Calibration-free Quantization via Learned Transformations and Adaptive Rounding
Ziteng Sun, Adrian Benton, Samuel Kushnir +4
Post-training quantization is an effective method for reducing the serving cost of large language models, where the standard approach is to use a round-to-nearest quantization leve…
QHDOPT: A Software for Nonlinear Optimization with Quantum Hamiltonian Descent
Samuel Kushnir, Jiaqi Leng, Yuxiang Peng +2
We develop an open-source, end-to-end software (named QHDOPT), which can solve nonlinear optimization problems using the quantum Hamiltonian descent (QHD) algorithm. QHDOPT offers…