Accepted Papers

Virtual Machines Scheduling using Reinforcement Learning in Cloud Data Centers. Xianzhong Ding, YUNKAI ZHANG, Binbin Chen, Tieying Zhang, Jianjun Chen, Ye Liu.

Drug Discovery Machine Learning Systems For Accelerating Idea Hypothesis To Production Decisions. Carsten Stahlhut, Jesper Ferkinghoff-Borg, Kang Li, Kilian W. Conde-Frieboes, Vanessa Isabell Jurtz, Christian Vind, Kristoffer Balling, Søren Berg Padkjær.

Towards Continually Learning Application Performance Models. Ray A. O. Sinurat, Anurag Daram, Haryadi Gunawi, Robert Ross, Sandeep Madireddy.

An MLIR-based Compiler for Interoperability between Machine Learning and Science Frameworks. Brian Kelley, Siva Rajamanickam.

Learning to Drive Software-Defined Storage. Jian Huang, Daixuan Li, Jinghan Sun.

Predicting Network Buffer Capacity for BBR Fairness. Ibrahim Umit Akgun, Santiago Vargas, Andrew Burford, Michael McNeill, Michael Arkhangelskiy, Aruna Balasubramanian, Anshul Gandhi, Erez Zadok.

Silhouette: Toward Performance-Conscious and Transferable CPU Embeddings. Abdul Wasay.

Power Consumption Estimation for Laptops - a Machine Learning Approach. Carlota Parés Morlans, Ruben Rodriguez Buchillon, Udaya Kiran Ammu, Puthikorn Voravootivat, Milad Hashemi.

Multi-Agent Join. Arash Termehchy, Vahid Ghadakchi, Mian Xie, Michael Burton.

LoopStack: ML-friendly ML Compiler Stack. Bram Wasti, Dejan Grubisic, Benoit Steiner, Aleksandar Zlateski.

Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR. Sami Alabed, Dominik Grewe, Juliana Franco, Bart Chrzaszcz, Tom Natan, Tamara Norman, Norman Rink, Dimitrios Vytiniotis, Michael Schaarschmidt.

Multi-objective Reinforcement Learning with Adaptive Pareto Reset for Prefix Adder Design. Jialin Song, Rajarshi Roy, Jonathan Raiman, Robert Kirby, Neel Kant, Saad Godil, Bryan Catanzaro.

A Framework for Network-Centric ML-Systems Datasets. Yara Awad.

Preference-Aware Constrained Multi-Objective Bayesian Optimization For Analog Circuit Design. Alaleh Ahmadian, Syrine Belakaria, Jana Doppa.

External Memory Is All You Need: Tiny Deep Learning on MCUs. Sulaiman Sadiq, Jonathon Hare, Simon Craske, Partha Maji, Geoff V. Merrett.

Wireless Parameter Tuning with Clustering Multi-Agents in Linear Stochastic Bandit. Hamza Cherkaoui, Merwan Barlier, Igor Colin.

The Case for Learning Machine Language. Guangda Liu, Chieh-Jan Mike Liang, Shijie Cao, Shuai Lu, Leendert van Doorn.

HloEnv: A Graph Rewrite Environment for Deep Learning Compiler Optimization Research. Chin Yang Oh, Kunhao Zheng, Bingyi Kang, Xinyi Wan, Zhongwen Xu, Shuicheng YAN, Min Lin, Yangzihao Wang.

Robust Scheduling with GFlowNets. David W Zhang, Corrado Rainone, Markus Peschl, Roberto Bondesan.

A code superoptimizer through neural Monte-Carlo tree search. Wenda Zhou, Olga Solodova, Ryan P Adams.

Target-independent XLA optimization using Reinforcement Learning. Milan Ganai, Haichen Li, Theodore Enns, Yida Wang, Randy Huang.

Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration. Srivatsan Krishnan, Natasha Jaques, Shayegan Omidshafiei, Dan Zhang, Izzeddin Gur, Vijay Janapa Reddi, Aleksandra Faust.

An Efficient One-Class SVM for Novelty Detection in IoT. Kun Yang, Samory Kpotufe, Nicholas Feamster.

MEOW: - Automatic Evolutionary Multi-Objective Concealed Weapon Detection. Daniel Dimanov, Colin Singleton, Shahin Rostami, Emili Balaguer-Ballester.

Sensitivity-Aware Finetuning for Accuracy Recovery on Deep Learning Hardware. Lakshmi Nair, Darius Bunandar.

Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs. Benjamin Fuhrer, Yuval Shpigelman, Chen Tessler, Shie Mannor, Gal Chechik, Eitan Zahavi, Gal Dalal.

NeuralFuse: Improving the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes. Hao-Lun Sun, Lei Hsiung, Nandhini Chandramoorthy, Pin-Yu Chen, Tsung-Yi Ho.

Lattice Quantization. Clément Metz, Thibault Allenet, Johannes Christian Thiele, Antoine DUPRET, Olivier BICHLER.

An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design. Mingjie Liu, Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Selim Dogru, Anima Anandkumar, David Z. Pan, Brucek Khailany, Haoxing Ren.

HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression. Jiaqi Gu, Ben Keller, Jean Kossaifi, Anima Anandkumar, Brucek Khailany, David Z. Pan.

* Equal Contribution