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AI at the Edge Workshop in IGSC

Dr. Merkel is organizing the 2nd workshop on AI at the Edge to be held in conjunction with the 2021 International Conference on Green and Sustainable Computing (

Artificial intelligence (AI) has established itself as a valuable tool to improve multiple aspects of human life.  Today, most AI applications leverage power-hungry cloud resources (e.g. GPUs) in order to meet their ever-growing memory and compute requirements.  While moving AI to the edge will unlock many new application domains, it remains very challenging to perform the required computations on size, weight, and power (SWaP)-constrained devices (mobile phones, sensors, etc.).  To help address the challenge, this workshop will bring together experts in multiple fields such as AI, integrated circuit design, device physics, and computer architecture to discuss the way forward in moving AI onto SWaP-constrained edge devices.  This workshop will complement a number of this year’s IGSC topics/themes including VLSI design, computer architecture, embedded design, low-power design, and ML for energy-efficient system design.

Topics include but are not limited to:

  • AI algorithm efficiency (quantization, compression, etc.)
  • Energy-efficient AI architectures
  • AI circuit optimization for size, weight, and power (SWaP) efficiency
  • Neuromorphic computing (based on conventional or emerging technologies)
  • Efficient (e.g. energy-efficient) edge-cloud interaction for AI applications
  • Brain-inspired machine learning algorithms


Contributions are limited to 8 pages (IEEE style) and will be published in IEEE Xplore.  Papers can be submitted through Easy Chair:  

Important Dates:

August 23, 2021 September 6: First submission due.

September 1, 2021 September 13: Final camera-ready submission due.

For quesions, please contact Cory Merkel:

Schedule (all in Pacific Time):

October 18, 2021

Zoom Link:

8:00-8:30    "Using Static and Dynamic Malware features to perform Malware Ascription" - Keshav Kumar and Jashanpreet Singh Sraw

8:30-9:00    "Prediction of Mental Stress Level Based on Machine Learning" - Akshada Kene and Shubhada Thakare

9:00-9:30    "Real-Time Evolution and Deployment of Neuromorphic Computing at the Edge" - Catherine Schuman, Steven Young, Bryan Maldonado and Brian Kaul

9:30-10:00   "Quasi-Gradient-Free Mehod for Training Neural Networks" - Cory Merkel

10:00-10:30  "Smart System to Answer Questions about Visual Content" -  Sasank Tadepalli, Mohan Sai Thulluri, Rishitha Pamula, Udayabhanu Petha and Ranga Rao Jalleda