Proceedings of the DATE Friday Workshop on Heterogeneous Architectures and Design Methods for Embedded Image Systems (HIS 2015)

no code implementations • 25 Feb 2015 • Frank Hannig, Dietmar Fey, Anton Lokhmotov

This volume contains the papers accepted at the DATE Friday Workshop on Heterogeneous Architectures and Design Methods for Embedded Image Systems (HIS 2015), held in Grenoble, France, March 13, 2015.

Collective Mind, Part II: Towards Performance- and Cost-Aware Software Engineering as a Natural Science

22 code implementations • 20 Jun 2015 • Grigori Fursin, Abdul Memon, Christophe Guillon, Anton Lokhmotov

Nowadays, engineers have to develop software often without even knowing which hardware it will eventually run on in numerous mobile phones, tablets, desktops, laptops, data centers, supercomputers and cloud services.

GEMMbench: a framework for reproducible and collaborative benchmarking of matrix multiplication

1 code implementation • 12 Nov 2015 • Anton Lokhmotov

We introduce GEMMbench, a framework and methodology for evaluating performance of GEMM implementations.

Mathematical Software Performance

Introducing ReQuEST: an Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments

7 code implementations • 19 Jan 2018 • Thierry Moreau, Anton Lokhmotov, Grigori Fursin

Co-designing efficient machine learning based systems across the whole hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming.

BIG-bench Machine Learning

A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques

3 code implementations • 19 Jan 2018 • Grigori Fursin, Anton Lokhmotov, Dmitry Savenko, Eben Upton

Developing efficient software and hardware has never been harder whether it is for a tiny IoT device or an Exascale supercomputer.

Human-Computer Interaction Computers and Society

MLPerf Inference Benchmark

4 code implementations • 6 Nov 2019 • Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, Ramesh Chukka, Cody Coleman, Sam Davis, Pan Deng, Greg Diamos, Jared Duke, Dave Fick, J. Scott Gardner, Itay Hubara, Sachin Idgunji, Thomas B. Jablin, Jeff Jiao, Tom St. John, Pankaj Kanwar, David Lee, Jeffery Liao, Anton Lokhmotov, Francisco Massa, Peng Meng, Paulius Micikevicius, Colin Osborne, Gennady Pekhimenko, Arun Tejusve Raghunath Rajan, Dilip Sequeira, Ashish Sirasao, Fei Sun, Hanlin Tang, Michael Thomson, Frank Wei, Ephrem Wu, Lingjie Xu, Koichi Yamada, Bing Yu, George Yuan, Aaron Zhong, Peizhao Zhang, Yuchen Zhou

Machine-learning (ML) hardware and software system demand is burgeoning.

Benchmarking

Benchmarking TinyML Systems: Challenges and Direction

2 code implementations • 10 Mar 2020 • Colby R. Banbury, Vijay Janapa Reddi, Max Lam, William Fu, Amin Fazel, Jeremy Holleman, Xinyuan Huang, Robert Hurtado, David Kanter, Anton Lokhmotov, David Patterson, Danilo Pau, Jae-sun Seo, Jeff Sieracki, Urmish Thakker, Marian Verhelst, Poonam Yadav

In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads.

Benchmarking Position

Dynamic Network selection for the Object Detection task: why it matters and what we (didn't) achieve

no code implementations • 27 May 2021 • Emanuele Vitali, Anton Lokhmotov, Gianluca Palermo

We benchmarked different neural networks to find the optimal detector for the well-known COCO 17 database, and we demonstrate that even if we only consider the quality of the prediction there is not a single optimal network.

object-detection Object Detection