Workshop On Energy Efficient Machine Learning And Cognitive Computing For Embedded Applications

Workshop On Energy Efficient Machine Learning And Cognitive Computing For Embedded Applications (EMC^2)
in conjunction with HPCA 2019
Washington DC, USA
February 17, 2019

IMPORTANT DATES:
Paper Submission: Jan 18, 2019 (11:59 pm PST)
Notification and Rebuttals: Feb 5, 2019
Final Version Due: Feb 8, 2019

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:
– Computing techniques for IoT, Automotive, and mobile intelligence
– Exploration new and efficient applications of machine learning
– Machine learning benchmarks, workloads and their characterization
– Energy efficient techniques and solutions for neural networks
– Efficient hardware proposals to implement neural networks
– Power and performance efficient memory architectures
– Exploring the interplay between precision, performance, power and energy
– Approximation, quantization and reduced precision computing techniques
– Improvements over conventional training techniques
– Hardware/software techniques to exploit sparsity and locality
– Security and privacy challenges and building secure systems

ORGANIZERS:
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

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