Call for Papers: Workshop on Emerging Deep Learning Accelerators
http://workshops.inf.ed.ac.uk/edla/
Submitted by Jose Cano

1st Workshop on Emerging Deep Learning Accelerators (EDLA)
co-located with HiPEAC 2019
January 21-23, 2019
Valencia, Spain

IMPORTANT DATES:
Submission Deadline: November 9, 2018
Notification of Acceptance: December 7, 2018
Camera-ready Deadline: until December 20, 2018

Deep Learning is receiving much attention these days due to remarkable performance achieved in several fields (e.g. Computer Vision, Speech, Translations, etc), although this brings some challenges to hardware architects and computation optimization researchers. Deep Learning models are generally very large in memory and require many computation instructions to train and perform inferences. 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. This workshop on Emerging Deep Learning Accelerators (EDLA) is intended to bring together researchers from academia and industry to discuss requirements, opportunities, challenges and next steps in developing novel approaches for accelerating deep neural networks. The timing of this workshop is ideal, with European regulations tightening data privacy, thus forcing more computations/inferences to be performed at the Edge.

Topics of interest include (but are not limited to):
– Novel parallel computing architectures​: GPUs, FPGAs, and heterogeneous multi/many-core designs.
– Crazy architectural ideas: focused on accelerating deep learning workloads/algorithms.
– Cloud and edge computing​: hardware and software methods focused on accelerating both training (cloud) and inference (edge).
– Compilers, tools, and programming models: focused on accelerating deep learning workloads/algorithms.

SUBMISSION GUIDELINES:
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 deep learning accelerators. 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.

ORGANIZERS:
José Cano – University of Glasgow
Valentin Radu – University of Edinburgh
David Gregg – Trinity College Dublin
Nuria Pazos – University of Applied Sciences (HES-SO)
Elliot Crowley – University of Edinburgh
Miguel de Prado – ETH Zurich
Jack Turner – University of Edinburgh
Andrew Mundy – ARM Research
Tim Llewellynn – NVISO

Call for Papers: DSN 2019
http://dsn.org/cfpapers.html
Submitted by Karthik Pattabiraman

The 49th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
Portland, Oregon, USA
June 24 – 27, 2019

IMPORTANT DATES:
Abstract submission deadline: Nov 30, 2018
Paper submission deadline: Dec 7, 2018
Author rebuttal period: Feb 12 – 14, 2019
Notification to authors: Mar 4, 2019

The annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) is the premier venue for results on dependable systems. It has pioneered the fusion between dependability and security research, understanding the need to simultaneously fight against accidental faults, intentional cyber-attacks, design errors, and unexpected operating conditions.

Its distinctive approach to both accidental and malicious faults made DSN the most prestigious international forum for presenting research furthering robustness and resilience of today’s wide spectrum of computing systems and networks, with dependability embracing security aspects under a common body of knowledge.

All aspects on the research and practice of dependability and applied security are within the scope of DSN. Relevant topics include innovative architectures, protocols, and algorithms, for preventing, detecting, recovering, diagnosing or eliminating accidental and malicious threats as well as experimentation with and assessment of dependable and secure systems and networks.

Authors are invited to submit original papers on the DSN’s current thematic areas of emphasis:
– Hardware (e.g., microprocessors, memories, system on chip, I/O devices, storage systems, trusted computing)
– Software (e.g., applications, middleware, operating systems)
– Cyber-physical systems (e.g., embedded systems, real-time critical systems, internet of things, smart grid, automotive, aerospace, medical, autonomous systems)
– Socio-technical systems (e.g., virtual worlds, human-computer interaction, social networking)
– Clouds (e.g., cloud storage, computing, platforms, applications, blockchain, cloud security and privacy)
– Networks (e.g., wireless networks, mobility, software-defined networking, edge computing, network on chip, intrusion detection systems, network security)
– Models and methodologies for programming, evaluating, verifying, and assessing dependable and secure systems (e.g., performance and dependability evaluation, analytical and numerical methods, simulation, experimentation, benchmarking, verification, field data analysis)

Research Papers, Practical Experience Reports, and Tool Descriptions will be refereed and included in the Proceedings of the DSN 2019, if accepted.

All contributions must be written in English. IEEE Computer Society will publish accepted contributions.

For more information, please visit the DSN web site (http://dsn.org/cfpapers.html) or contact pc_chairs@dsn.org

Program Committee Co-Chairs:
Karthik Pattabiraman, University of British Columbia (UBC)
Fernando Pedone, University of Lugano (USI)

Call for Papers: VEE 2019
https://conf.researchr.org/home/vee-2019
Submitted by Jennifer B. Sartor

The 15th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE’19)
co-located with ASPLOS
Providence, Rhode Island, United States
April 13-14, 2019

The International Conference on Virtual Execution Environments (VEE’19) brings together researchers and practitioners from different computer systems domains to interact and share ideas in order to advance the state of the art of virtualization and broaden its applicability. Virtualization plays a central role in modern systems. It constitutes a key aspect in a wide range of environments, from small mobile computing devices to large-scale data centers and computational clouds. Virtualization techniques encompass the underlying hardware, the operating system, and the runtime system. Although these layers have different design and implementation techniques, the fundamental challenges and insights tend to be similar.

VEE’19 accepts both full-length and short papers. Short papers are limited to half the space of full-length papers. An ideal short paper would express an idea that doesn’t require 12 pages to describe or evaluate. The ideas should be well formed and complete just like those in a full-length paper. Short papers will be held to the same standard as full-length papers in terms of clarity of presentation and evaluation, however, the scope of the work as well as the breadth of the evaluation is expected to be smaller.

We invite authors to submit original papers related to virtualization across all layers of the software stack down to the microarchitectural level. Topics of interest include (but are not limited to):
– architectural support for virtualization
– compiler and programming language support for virtualization
– managed runtimes and virtual machines
– management technologies for virtual environments
– memory management
– operating system support for virtualization
– performance analysis and debugging for virtual environments
– runtime system support for virtualization
– security and virtual environments
– virtual I/O, storage, and networking
– virtualization in cloud computing
– virtualization support for programs and programmers
– virtualization technologies applied to specific problem domains such as HPC, realtime, and power management

IMPORTANT DATES (UTC-12h):
Abstract Deadline: Dec 7, 2018
Submission Deadline: Dec 14, 2018
Author Response: Jan 25-29, 2019
Notification: Feb 4, 2019

ORGANIZERS:
General Chair:
Jennifer B. Sartor – Ghent University and Vrije Universiteit Brussel

Program Co-chairs:
Christopher J. Rossbach – University of Texas at Austin and VMware Research
Mayur Naik – University of Pennsylvania

Program Committee:
Anil Madhavapeddy, University of Cambridge
Baris Kasikci, University of Michigan
Dan Williams, IBM T. J. Watson
Dilma Da Silva, Texas A&M University
Eric Schkufza, VMware Research
Hyesoon Kim, Georgia Tech
Jan Vitek, Northeastern University
Jonathan Appavoo, Boston University
Kevin Pedretti, Sandia National Laboratories
Mark Silberstein, Technion
Naila Farooqui, NVIDIA
Paolo Bonzini, Red Hat
Peter Pietzuch, Imperial College London
Priyanka Tembey, VMware
Rong Chen, Shanghai Jiao Tong University
Timothy Merrifield, VMware
Weidong Cui, Microsoft Research
Youngjin Kwon, KAIST
Yunheung Paek, Seoul National University

Call for Papers: Workshop on Thermodynamic Computing
https://cra.org/ccc/events/thermodynamic-computing/
Submitted by Khari Douglas

Computing Community Consortium (CCC) Workshop on Thermodynamic Computing
Honolulu, Hawaii, USA
January 3-5, 2019

IMPORTANT DATES:
Submission Deadline: October 5, 2018 (11:59pm PST) – contact Khari Douglas at kdouglas@cra.org to request an extension
Acceptance Notification: October 26, 2018

The Computing Community Consortium (CCC) will hold a workshop from January 3rd to 5th, 2019 in Hawaii to create a vision for thermodynamic computing, a statement of research needs, and a summary of the current state of understanding of this new area. Workshop attendance will be by invitation only and travel expenses will be available for select participants. We seek short white papers to help create the agenda for the workshop and select attendees.

Thermodynamics has a long history in the engineering of computing systems due to its role in power consumption, scaling, and device performance [1],[2]. In a different context, thermodynamically motivated algorithmic techniques are prevalent and highly successful in areas such as machine learning [3], simulated annealing [4], and neuromorphic systems. The foundational thinking underlying much of the existing technology derives largely from equilibrium properties of closed thermodynamic systems. We aim to foster a community to extend these foundations into the domain of non-equilibrium thermodynamics toward the development of a new class of technologies that we call open thermodynamic computers.

The overall intuition is that striving for thermodynamic efficiency is not only highly desirable in hardware components, but may also be used as an embedded capability in the creation of algorithms: can dissipated heat be used to trigger adaptation/restructuring of (parts of) the functioning hardware, thus allowing hardware to evolve increasingly efficient computing strategies? Recent theoretical developments in non-equilibrium thermodynamics suggest that thermodynamics drives the organization of open systems as a natural response to external input potentials; that is, that these systems adapt as they dissipate energy, enter low dissipation homeostatic states and as a result ‘learn’ to ‘predict’ future inputs [5],[6]. For example, lower bounds on thermodynamic efficiency in driven systems (away from equilibrium), indicate that systems have to retain relevant, predictive information in order to be thermodynamically efficient [7],[8]. This strategy is, of course, the same as what is followed in machine learning (and, in general, in science): predictive inference [9]. This interesting connection between energy efficiency and information processing inspires us to bring together researchers in the various disciplines with the goal of building the foundations that would allow us to build radically different computing systems.

This CCC workshop will gather a set of leading researchers working to define open thermodynamic computers, to describe the reasons that they should be studied, to enumerate the major challenges that lay before us, and to create a strategy for a way forward. We seek a diverse group of physical theorists, electrical and computer engineers, and electronic / ionic device researchers with strong understanding of thermodynamics.

For this workshop, we request white papers of no more than two pages. Topics of interest include, but are not limited to:
– Non-equilibrium thermodynamic theory and its implications for self-organizing computing technologies, such as fluctuation-dissipation theorems and their ramifications regarding homeostasis, learning by prediction, stability, and plasticity.
– Novel device and component concepts that change and retain state (learn) in response to the thermodynamics of their environment, which may be suitable as the evolvable elements for future for thermodynamic computers.
– System concepts that integrate human-directed programmability and thermodynamically evolvable elements.
– Concepts to integrate and embed thermodynamic computers into an open environment of electrical and information potential.
– Non-equilibrium, thermodynamically inspired machine learning techniques and algorithms, such as simulated annealing in complex non-equilibrium environments and unsupervised learning by prediction.
– Challenge problems to motivate the development of thermodynamic computers.

Topics out of scope include:
– Machine learning accelerators.
– Established machine-learning techniques.
– Brain simulations / models / simulators.
– Novel, programmable computing systems.

Authors of white papers may be asked to participate in teleconferences to develop an agenda prior to the workshop.

SUBMISSION GUIDELINES:
Please submit your white paper by October 5th 2018 via this Wufoo registration form: https://computingresearch.wufoo.com/forms/s7cfi0n14q5re8/. For more information, please visit https://cra.org/ccc/events/thermodynamic-computing/. The organizing committee will notify the selected attendees by October 26th. Should you have any questions, please contact Khari Douglas at kdouglas@cra.org.

ORGANIZERS:
Todd Hylton, Prof of Practice ECE, UC San Diego
Tom Conte, Prof. of Computer Science, Georgia Inst. of Technology
Susanne Still, Prof. of Information & Computer Science, Univ. of Hawaii, Minoa
John Paul Strachan, Hewlett Packard Labs, HPE
Erik DeBenedictis,mSandia National Laboratories
Natesh Ganesh, Univ. of Massachusetts, Amherst
Stan Williams, stan@rstanleywilliams.com

REFERENCES:
1. Frank, Michael P. “The physical limits of computing.” Computing in Science & Engineering 4.3 (2002): 16-26.
2. Lloyd, Seth. “Ultimate physical limits to computation.” Nature 406.6799 (2000): 1047.
3. Dayan, Peter, et al. “The helmholtz machine.” Neural computation 7.5 (1995): 889-904.
4. Černý, Vladimír. “Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm.” Journal of optimization theory and applications45.1 (1985): 41-51.
5. N. Ganesh, “A Thermodynamic Treatment of Intelligent Systems,” 2017 IEEE International Conference on Rebooting Computing (ICRC), Washington, DC, 2017, pp. 1-4.
6. Perunov, Nikolay, Robert A. Marsland, and Jeremy L. England. “Statistical physics of adaptation.” Physical Review X6.2 (2016): 021036.
7. Still, Susanne, et al. “Thermodynamics of prediction.” Physical review letters109.12 (2012): 120604.
8. Still, Susanne. “Thermodynamic cost and benefit of data representations.” arXiv preprint arXiv:1705.00612 (2017).
9. Still, Susanne. “Information bottleneck approach to predictive inference.” Entropy16.2 (2014): 968-989.

Call for Papers: ML for Systems Workshop
http://mlforsystems.org/
Submitted by Milad Hashemi

ML for Systems Workshop
in conjunction with NIPS 2018
Montreal, Canada
December 8, 2018

Submission Deadline: October 19, 2018

Designing specialized hardware for deep learning is a topic that has received significant research attention, both in industrial and academic settings, leading to exponential increases in compute capability in GPUs and accelerators. However, using machine learning to optimize and accelerate software and hardware systems is a lightly explored but promising field, with broad implications for computing as a whole. Very recent work has outlined a broad scope where deep learning vastly outperforms traditional heuristics including topics such as: scheduling, data structure design, microarchitecture, compilers, control of warehouse scale computing systems, and auto-tuned software infrastructure.

The focus of this workshop is to expand upon this recent work and build a community focused on using machine learning in computer systems problems. We seek to improve the state of the art in the areas where learning has already proven to perform better than traditional heuristics, as well as expand to new areas throughout the system stack such as hardware/circuit design and operating/runtime systems.

We welcome submission of up to 4-page extended abstracts in the broad area of using machine learning to accelerate, design, or architect computer systems and software. Accepted papers will be made available on the workshop website, but there will be no formal proceedings. Authors may therefore publish their work in other journals or conferences. The workshop will include invited talks from industry and academia as well as oral and poster presentations by participants.

The workshop has a pool of NIPS registrations that will be awarded to the authors of accepted submissions.

Areas of interest:
– Supervised, unsupervised, and reinforcement learning research with applications to:
– Systems Software
– Runtime Systems
– Distributed Systems
– Security
– Compilers, data structures, and code optimization
– Computer architecture, microarchitecture, and accelerators
– Circuit design and layout
– Interconnects and Networking
– Storage
– Datacenters
– Representation learning for hardware and software
– Optimization of computer systems and software
– Systems modeling and simulation
– Implementations of ML for Systems and challenges
– High quality datasets for ML for Systems problems

SUBMISSION GUIDELINES:
We welcome submissions of up to 4 pages (not including references). This is not a strict limit, but authors are encouraged to adhere to it if possible. All submissions must be in PDF format and should follow the NIPS 2018 format. Submissions do not have to be anonymized.

Please submit your paper no later than October 19th, 2018 to CMT at: https://cmt3.research.microsoft.com/NIPSMLforSystems2018

IMPORTANT DATES:
Submission Deadline: October 19, 2018
Acceptance Notifications: November 9, 2018
Final File Upload: To be Announced
Workshop: December 8th, 2018

Contact us at mlforsystems@googlegroups.com.

Call for Papers: ISCA 2019
http://iscaconf.org/isca2019
Submitted by Carole-Jean Wu

International Symposium on Computer Architecture (ISCA)
Phoenix, Arizona, USA
Jun 22-26, 2018

IMPORTANT DATES:
Abstract Registration (Mandatory): December 3, 2018, 8:00 PM EST
Submission Deadline (No extensions): December 7, 2018, 8:00 PM EST
Author Rebuttal Period: February 22-29, 2019
Notification Date: Mar 15, 2019
Final Manuscripts Due: Apr 15, 2019

The International Symposium on Computer Architecture is the premier forum for new ideas and experimental results in computer architecture. The conference specifically seeks particularly forward-looking and novel submissions. Papers are solicited on a broad range of topics, including (but not limited to):
– Processor, memory, and storage systems architecture
– Multiprocessor systems
– Datacenter-scale computing
– IoT, mobile and embedded architecture
– Interconnection network, router and network interface architecture
– Accelerator-based, application-specific and reconfigurable architecture
– Architectural support for programming languages or software development
– Architectural support for security or virtualization
– Instruction, thread and data-level parallelism
– Dependable processor and system architecture
– Architecture for emerging technologies and applications, including quantum computing, novel memory technologies, etc.
– Effects of circuits or technology on architecture
– Architecture modeling and simulation methodologies
– Evaluation and measurement of real computing systems

ORGANIZERS:
General Chair: Srilatha (Bobbie) Manne, Microsoft
Program Chair: Hillery Hunter, IBM

Call for Papers: CC 2019
https://cc-conference.github.io/19/
Submitted by Milind Kulkarni

International Conference on Compiler Construction (CC)
co-located with CGO and PPoPP and HPCA
Washington, DC, USA
February 16-17, 2019

IMPORTANT DATES:
(All deadlines AoE)
Abstract Submission: November 3, 2018
Paper Submission: November 7, 2018
Author Response: December 14-15, 2018
Notification: January 1, 2018
Final papers due: January 15, 2018

The International Conference on Compiler Construction (CC) is interested in work on processing programs in the most general sense: analyzing, transforming or executing input that describes how a system operates, including traditional compiler construction as a special case.

Original contributions are solicited on the topics of interest which include, but are not limited to:
– Compilation and interpretation techniques, including program representation, analysis, and transformation; code generation, optimization, and synthesis; the verification thereof
– Run-time techniques, including memory management, virtual machines, and dynamic and just-in-time compilation
– Programming tools, including refactoring editors, checkers, verifiers, compilers, debuggers, and profilers
– Techniques for specific domains, such as secure, parallel, distributed, embedded or mobile environments
– Design and implementation of novel language constructs, programming models, and domain-specific languages

CC 2019 is the 28th edition of the conference. It will be co-located with CGO, HPCA, and PPoPP and take place during 16-17 February in Washington, DC, USA

Conference information, including conference committee and paper submission instructions, can be found at https://cc-conference.github.io/19/call/

Call for Papers: SysML 2019
http://www.sysml.cc/
Submitted by Vivienne Sze

Conference on Systems and Machine Learning (SysML)
Stanford, CA, USA
March 31–April 2, 2019

Submissions due: September 28, 2018

SysMLis a conference on research at the intersection of computer systems and machine learning. The conference aims to create new connections across the systems and machine learning fields, including identifying design principles for learning systems and developing novel learning methods and theory tailored to practical ML workflows. SysML’s program committee brings together experts from both systems and ML to evaluate work from the perspective of both communities.

The inaugural SysML conference (www.sysml.cc/2018/) in February 2018 sold out, with 300 attendees from academia and industry and over 200 submissions.

SUBMISSION GUIDELINES:
Papers should be up to 10 pages and are due September 28, 2018. Reviewing will be double-blind and papers will be published in conference proceedings. SysML will also include an optional artifact evaluation process to highlight research with reproducible experiments, data, and software artifacts.

For more details, see www.sysml.cc/#submission.