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The Cross-Layer Approach to Efficient Machine Intelligence
The Cross-Layer Approach to Efficient Machine Intelligence With Device-to-Algorithm Co-Design
  • Machine Learning (ML) Hardware Acceleration (with beyond-CMOS technologies)

Providing efficient computational systems to meet the growing demands from processing machine learning (ML) tasks presents exciting opportunities for research across the computational stack from Hardware to Algorithm. I would like to investigate how to enable energy-efficient and robust artificial intelligence (AI) through a co-design approach involving device, circuit, architecture, and algorithm.

Major thrusts include but not limited to:

Device-architecture-algorithm co-optimization of in-memory computing systems

Integration and optimization of emerging memory technologies for application-specific architecture (such as systolic arrays)

Hardware-aware network architecture search and pruning

  • Neurmorphic Computing and Brain-Inspired Computational Models

At present, researchers in ML and neuromorphic communities are striving to meet the challenge of building intelligent electronic hardware that can reach brain-like cognitive abilities with a similar level of efficiency and robustness. I am interested in gaining inspirations from the biological systems towards developing better systems for processing complex cognitive tasks. While deep neural networks have demonstrated remarkable success, I am interested in exploring various computational models that may achieve improvements in efficiency, robustness, and explainability. Since conventional CMOS gates are not designed for such types of AI workloads, I will explore hardware implementations of neuromorphic computing with diverse device/material technologies.

Major thrusts include but not limited to:

Developing efficient and robust spiking neural networks for various AI tasks (vision and language)

Exploration of unconventional computational models such as oscillatory activation and stochastic computing

Leveraging emerging device physics to provide highly efficient neuromorphic functionality 

  • Nanoelectronics and Spintronics for Efficient AI

As AI model size and amount of data being processed grow exponentially over the recent years, the urge of developing efficient AI is stronger than ever.nThe exciting development in beyond-CMOS devices and materials provide us a gold mine for prototyping novel AI hardware. However, it remains to be seen that if any of the novel technologies can deliver the next giant leap in hardware technology. Meanwhile, the rich dynamics in various nano-scale systems may also give us inspirations for representing and processing information.

Interested directions: 

Searching for new mechanisms in nanoelectronic and nonmagnetic devices for efficient building blocks in AI computation

Enabling cross fertilization between hardware technologies and intelligent algorithms