==================================================================
3rd Workshop on Accelerated Machine Learning (AccML)
Co-located with the HiPEAC 2021 Conference
(https://www.hipeac.net/2021/budapest/)
January 18, 2021
Online Event
==================================================================
————————————————————————-
REGISTRATION INFORMATION
————————————————————————-
– Registration allows full access to all live events (keynotes, paper tracks, workshops/tutorials, etc) that are part of HiPEAC 2021.
– You need a HiPEAC account to register for the conference (very low fees).
– Registration Link: https://www.hipeac.net/2021/budapest/#/registration/
————————————————————————-
GENERAL INFORMATION
————————————————————————-
The remarkable performance achieved in a variety of application areas (natural language processing, computer vision, games, etc.) has led to the emergence of heterogeneous architectures to accelerate machine learning workloads. In parallel, production deployment, model complexity and diversity pushed for higher productivity systems, more powerful programming abstractions, software and system architectures, dedicated runtime systems and numerical libraries, deployment and analysis tools. Deep learning models are generally memory and computationally intensive, for both training and inference. Accelerating these operations has obvious advantages, first by reducing the energy consumption (e.g. in data centers), and secondly, making these models usable on smaller devices at the edge of the Internet. In addition, while convolutional neural networks have motivated much of this effort, numerous applications and models involve a wider variety of operations, network architectures, and data processing. These applications and models permanently challenge computer architecture, the system stack, and programming abstractions. The high level of interest in these areas calls for a dedicated forum to discuss emerging acceleration techniques and computation paradigms for machine learning algorithms, as well as the applications of machine learning to the construction of such systems.
In this 3rd AccML workshop, we aim to bring together researchers and practitioners working on computing systems for machine learning, and using machine learning to build better computing systems. It also reaches out to a wider community interested in this rapidly growing area, to raise awareness of the existing efforts, to foster collaboration and the free exchange of ideas.
————————————————————————-
PROGRAM
————————————————————————-
4 Invited talks:
– Enabling innovation for the AI future (Jem Davies, ARM):
– How to Evaluate Efficient Deep Neural Network Approaches (Vivienne Sze, MIT)
– Advanced software and compilation techniques in ML (David Lacey, Graphcore)
– INertial Sensor Neural Computing Acrobatics (Danilo Pau, STMicroelectronics)
5 paper talks:
– Understanding Cache Boundness of ML Operators on ARM Processors (Bernhard Klein, Christoph Gratl, Manfred Mücke and Holger Fröning)
– Using the Graphcore IPU for traditional HPC applications (Thorben Louw and Simon McIntosh-Smith)
– NPS: A Compiler-aware Framework of Unified Network Pruning for Beyond Real-Time Mobile Acceleration(Zhengang Li, Geng Yuan, Wei Niu, Yanyu Li, Pu Zhao, Yuxuan Cai, Xuan Shen, Zheng Zhan, Zhenglun Kong, Qing Jin, Bin Ren, Yanzhi Wang and Xue Lin)
– Neural Pruning Search for Real-Time Object Detection of Autonomous Vehicles(Pu Zhao, Geng Yuan, Yuxuan Cai, Wei Niu, Bin Ren, Yanzhi Wang and Xue Lin)
– BlinkNet: Software-Defined Deep Learning Analytics with Bounded Resources(Brian Koga, Theresa Vanderweide, Xinghui Zhao and Xuechen Zhang)
EU project presentations:
– More details to be announced
Full program details in the workshop pages:
– Organizers: http://workshops.inf.ed.ac.uk/accml/
– HiPEAC: https://www.hipeac.net/2021/budapest/#/program/sessions/7837/
————————————————————————-
ORGANIZERS
————————————————————————-
José Cano (University of Glasgow)
Valentin Radu (University of Sheffield)
José L. Abellán (Catholic University of Murcia)
Marco Cornero (DeepMind)
Albert Cohen (Google)
Dominik Grewe (DeepMind)
Alex Ramirez (Google)