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05/27/2021
Dynamic Network selection for the Object Detection task: why it matters and what we (didn't) achieve
In this paper, we want to show the potential benefit of a dynamic auto-t...
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03/10/2020
Benchmarking TinyML Systems: Challenges and Direction
Recent advancements in ultra-low-power machine learning (TinyML) hardwar...
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11/06/2019
MLPerf Inference Benchmark
Machine-learning (ML) hardware and software system demand is burgeoning....
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06/19/2018
A model-driven approach for a new generation of adaptive libraries
Efficient high-performance libraries often expose multiple tunable param...
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01/19/2018
A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques
Developing efficient software and hardware has never been harder whether...
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01/19/2018
Introducing ReQuEST: an Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments
Co-designing efficient machine learning based systems across the whole h...
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11/12/2015
GEMMbench: a framework for reproducible and collaborative benchmarking of matrix multiplication
The generic matrix-matrix multiplication (GEMM) is arguably the most pop...
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02/25/2015