EMC2: The 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing Vancouver Convention Center Vancouver, Canada, December 13, 2019 |
Conference website | https://www.emc2-workshop.com/neurips-19 |
Submission link | https://easychair.org/conferences/?conf=emc22 |
Submission deadline | November 15, 2019 |
EMC2: The 5th workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications Vancouver Convention Center, Vancouver CANADA , December 13, 2019 |
Conference website | https://www.emc2-workshop.com/neurips-19 |
Submission link | https://easychair.org/conferences/?conf=emc22 |
Topics: machine learning computer architecture embedded system low power computing
IMPORTANT DATES:
Paper submission: September 16, 2019 (11:59 pm PST)
Acceptance and rebuttals: September 30, 2019
Camera ready: October 11, 2019
Workshop: December 13, 2019
CALL FOR PAPERS:
A new wave of intelligent computing, driven by recent advances in machine learning and cognitive algorithms coupled with process technology and new design methodologies, has the potential to usher unprecedented disruption in the way conventional computing solutions are designed and deployed. These new and innovative approaches often provide an attractive and efficient alternative not only in terms of performance but also power, energy, and area. This disruption is easily visible across the whole spectrum of computing systems ranging from low end mobile devices to large scale data centers and servers.
A key class of these intelligent solutions is providing real-time, on-device cognition at the edge to enable many novel applications including vision and image processing, language translation, autonomous driving, malware detection, and gesture recognition. Naturally, these applications have diverse requirements for performance, energy, reliability, accuracy, and security that demand a holistic approach to designing the hardware, software, and intelligence algorithms to achieve the best power, performance, and area (PPA).
The goal of this workshop is to provide a forum for researchers who are exploring novel ideas in the field of energy efficient machine learning and artificial intelligence for embedded applications. We also hope to provide a solid platform for forging relationships and exchange of ideas between the industry and the academic world through discussions and active collaborations.
Below is a set of suggested but not limited topics:
- Architectures for the edge: IoT, automotive, and mobile
- Approximation, quantization reduced precision computing
- Hardware/software techniques for sparsity
- Neural network architectures for resource constrained devices
- Neural network pruning, tuning and and automatic architecture search
- Novel memory architectures for machine learing
- Communication/computation scheduling for better performance and energy
- Load balancing and efficient task distribution techniques
- Exploring the interplay between precision, performance, power and energy
- Exploration of new and efficient applications for machine learning
- Characterization of machine learning benchmarks and workloads
- Performance profiling and synthesis of workloads
- Simulation and emulation techniques, frameworks and platforms for machine learning
- Power, performance and area (PPA) based comparison of neural networks
- Verification, validation and determinism in neural networks
- Efficient on-device learning techniques
- Security, safety and privacy challenges and building secure AI systems
Organizing Committee
Raj Parihar, Tensilica/Cadence
Michael Goldfarb, Nvidia
Satyam Srivastava, Intel
Mahdi N. Bojnordi, University of Utah
Krishna Nagar, Intel
Tao Sheng, Amazon
Debu Pal, Cadence
Sushant Kondguli, Samsung
Ananya Pareek, Apple
Sikandar Mashayak, Wave
Program Committee
Raj Parihar, Tensilica/Cadence
Michael Goldfarb, Nvidia
Chen Ding, University of Rochester
Mahdi N. Bojnordi, University of Utah
Andy Glew, Nvidia
Sreepathi Pai, University of Rochester
Raj Jain, Washington University in St. Louis
Smruti R Sarangi, IIT Delhi
Shaoshan Liu, PerceptIn
Ali Shafiee, Samsung
Satyam Srivastava, Intel
Danian Gong, Cadence
Krishna Nagar, Intel
Tao Sheng, Amazon
Venue
The conference will be held in Vancouver, BC, Canada.
Contact
All questions about submissions should be emailed to submission chairs Satyam Srivastava or Tao Sheng at satyam.srivastava@intel.com, tsheng@amazon.com.