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Workshop on Accelerated Machine Learning (AccML)
Co-located with the HiPEAC 2020 Conference
(https://www.hipeac.net/2020/bologna/)
January 20, 2020
Bologna, Italy
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UPDATE: DEADLINE EXTENSION TO NOVEMBER 22, 2019
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CALL FOR CONTRIBUTIONS
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In the last 5 years, 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.
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Links to the Workshop pages
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HiPEAC: https://www.hipeac.net/2020/bologna/#/schedule/sessions/7739/
Organizers: http://workshops.inf.ed.ac.uk/accml/
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Speakers
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* Keynote speaker: Luca Benini (ETH Zurich and U. di Bologna)
Title: Extreme Edge AI on Open Hardware
Abstract: Edge Artificial Intelligence (AI) is the new mega-trend, as
privacy concerns and network bandwidth/latency bottlenecks prevent cloud
offloading of AI functions in many application domains, from autonomous
driving to advanced prosthetics. Hence we need to push AI toward sensors
and actuators. I will give an overview of recent efforts in developing
systems of-on-chips based on open source hardware and capable of
significant analytics and AI functions "at the extreme edge", i.e.
within the limited power budget of traditional microcontrollers that can
be co-located and integrated with the sensors/actuators themselves.
These open, extreme edge AI platforms create an exciting playground for
research and innovation.
Bio: Luca Benini holds the chair of digital Circuits and systems at ETHZ
and is Full Professor at the Universita di Bologna. He received a PhD
from Stanford University. In 2009-2012 he served as chief architect in
STmicroelectronics France. Dr. Benini's research interests are in
energy-efficient computing systems design, from embedded to
high-performance. He is also active in the design of ultra-low power
VLSI Circuits and smart sensing micro-systems. He has published more
than 1000 peer-reviewed papers and five books. He is an ERC-advanced
grant winner, a Fellow of the IEEE, of the ACM and a member of the
Academia Europaea. He is the recipient of the 2016 IEEE CAS Mac Van
Valkenburg award and of the 2019 IEEE TCAD Donald O. Pederson Best
Paper Award.
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* Invited speaker: Carole-Jean Wu (Facebook AI, Arizona State University)
Title: Machine Learning at Scale
Abstract: Machine learning systems are being widely deployed in
production datacenter infrastructure and over billions of edge devices.
This talk seeks to address key system design challenges when scaling
machine learning solutions to billions of people. What are key
similarities and differences between cloud and edge infrastructure? The
talk will conclude with open system research directions for deploying
machine learning at scale.
Bio: Carole-Jean Wu is a Research Scientist at Facebook’s AI
Infrastructure Research. She is also a tenured Associate Professor of
CSE in Arizona State University. Carole-Jean’s research focuses in
Computer and System Architectures. More recently, her research has
pivoted into designing systems for machine learning. She is the leading
author of “Machine Learning at Facebook: Understanding Inference at the
Edge” that presents unique design challenges faced when deploying ML
solutions at scale to the edge, from over billions of smartphones to
Facebook’s virtual reality platforms. Carole-Jean received her Ph.D. and
M.A. from Princeton and B.Sc. from Cornell.
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* Invited speaker: Albert Cohen (Google, Paris)
Title: Abstractions, Algorithms and Infrastructure for Post-Moore
Optimizing Compilers
Abstract: MLIR is a recently announced open source infrastructure to
accelerate innovation in machine learning (ML) and high-performance
computing (HPC). It addresses the growing software and hardware
fragmentation across machine learning frameworks, enabling machine
learning models to be consistently represented and executed on any type
of hardware. It also unifies graph representations and operators for ML
and HPC. It facilitates the design and implementation of code
generators, translators and optimizations at different levels of
abstraction and also across application domains, hardware targets and
execution environments. We will share our vision, progress and plans in
the MLIR project, zooming in on graph-level and loop nest optimization
as illustrative examples.
Bio: Albert Cohen is a research scientist at Google. He worked as a
research scientist at Inria from 2000 to 2018. He graduated from École
Normale Supérieure de Lyon and received his PhD from the University of
Versailles in 1999 (awarded two national prizes). He has also been a
visiting scholar at the University of Illinois, an invited professor at
Philips Research, and a visiting scientist at Facebook Artificial
Intelligence Research. Albert Cohen works on parallelizing and
optimizing compilers, parallel programming languages and systems, and
synchronous programming for reactive control systems. He served as the
general or program chair of some of the main conferences in the area and
a member of the editorial board of two journals. He co-authored more
than 180 peer-reviewed papers and has been the advisor for 26 PhD
theses. Several research projects led by Albert Cohen resulted in
effective transfer to production compilers and programming environments.
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* Invited speaker: Rune Holm (Arm)
Title: Big neural networks in small spaces; Towards end-to-end
optimisation for ML at the edge
Abstract: Neural networks have taken over use case after use case, from
image recognition, speech recognition, image enhancement to driving
cars, and show no sign of letting up. Yet so many of these use cases are
done by acquiring data and sending it off to the cloud for inference.
On-device ML brings unprecedented capabilities and opportunities to edge
devices with improved privacy, security, and reliability. This talk
explores the many aspects of system optimisation for edge ML, from
training-time optimisation, to the compilation of neural networks, to
the design of machine learning hardware, and looks at ways to save
execution time and memory footprint while preserving accuracy.
Bio: Rune Holm has been part of the semiconductor industry for more than
a decade. He started out on Mali GPUs, doing GPU microarchitecture and
designing shader compilers for VLIW cores. He then moved on to research
into experimental GPGPU designs and architectures targeting HPC, machine
learning and computer vision. He’s currently part of the Arm Machine
Learning Group, focusing on neural network accelerator architecture and
compilers optimising for these designs.
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Topics
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Topics of interest include (but are not limited to):
- Novel ML systems?: heterogeneous multi/many-core systems, GPUs, FPGAs;
- Software ML acceleration: languages, primitives, libraries, compilers
and frameworks;
- Novel ML hardware accelerators and associated software;
- Emerging semiconductor technologies with applications to ML hardware
acceleration;
- ML for the construction and tuning of systems;
- Cloud and edge ML computing: hardware and software to accelerate
training and inference;
- Computing systems research addressing the privacy and security of
ML-dominated systems.
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Submission
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Papers will be reviewed by the workshop's technical program committee
according to criteria regarding a submission's quality, relevance to the
workshop's topics, and, foremost, its potential to spark discussions
about directions, insights, and solutions in the context of accelerating
machine learning. Research papers, case studies, and position papers are
all welcome.
In particular, we encourage authors to submit works-In-Progress papers:
To facilitate sharing of thought-provoking ideas and high-potential
though preliminary research, authors are welcome to make submissions
describing early-stage, in-progress, and/or exploratory work in order to
elicit feedback, discover collaboration opportunities, and generally
spark discussion.
The workshop does not have formal proceedings.
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Important Dates
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Submission deadline: November 22, 2019
Notification of decision: December 6, 2019
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Organizers
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José Cano (University of Glasgow)
Valentin Radu (University of Edinburgh)
Marco Cornero (DeepMind)
Albert Cohen (Google)
Olivier Temam (DeepMind)
Alex Ramirez (Google)
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