Machine learning and artificial intelligence (ML/AI) program highlights the advances in the field with a focus on design and design automation at the cross section between ML/AI algorithms and hardware. While artificial intelligence and artificial neural network research has been ongoing for more than half a century, recent advances in accelerating the pace and scale of machine learning enabled by tensor-flow based gradient optimization in deeply layered convolutional networks (convnets) are revolutionizing the impact of artificial intelligence on every aspect of our daily lives, ranging from smart consumer electronics and services to self-navigating cars and personalized medicine.
The advances in deep learning are fueled by computing architectures tailored to the distributed nature of learning and inference in neural networks, akin to the distributed nature of neural information processing and synaptic plasticity in the biological brain. Neuromorphic brain-inspired electronics for ML/AI aim at porting the brain's efficacy, efficiency, and resilience to noise and variability to electronic equivalents in standard CMOS and emerging technologies, offering new design challenges and opportunities to advance computing architecture beyond Moore's law scaling limits.
The ML/AI sessions at DAC focuses on the fundamentals, accomplishments to date, and challenges ahead in ML/AI hardware system design and design automation, providing a forum for researchers and practitioners across all the widely varying disciplines involved to connect, engage, and join in shaping the future of this exciting field.
Call For Contributions
Research Paper Abstract Submission Deadline: November 21
Research Paper Manuscript Submission Deadline: November 27