Objective
Driven by the AI boom, technologies such as robotics, virtual reality, and self-driving have begun to emerge, leading to an ever-increasing demand for high-efficiency, data-intensive computing. Inspired by the brain, post-von-Neumann neuromorphic computing was proposed. Mapping the biological neural networks to implement neuromorphic computing requires both algorithm and hardware designs. Although the algorithm approach is being widely exploited, neuromorphic hardware is still in its early stages of development, requiring continual advancements in the underlying materials, devices, and circuits. This course covers the most up-to-date research in the field of neuromorphic hardware. The course will begin with an introduction to neuromorphic computing, followed by discussions on neuromorphic hardware development, in which the students will study the materials, devices, and circuits exploited to enable neuromorphic hardware. In addition to lectures, the course includes lab sessions where students can have experience with neuromorphic hardware fabrication.
Syllabus
Introduction to neuromorphic computing; Review on semiconductors, semiconductor devices and circuits; von Neumann architecture; Hardware implementations of artificial synapses and neurons; Device and circuit design and fabrication for neuromorphic hardware.
Learning Outcome
Upon successful completion of the course, students will be able to
- Be familiar with the von Neumann and non-von Neumann hardware architectures
- Gain the key fundamental knowledge of semiconductors, semiconductor devices and circuits to implement computation
- Apply the fundamental semiconductor knowledge to the design and implementation of the biological synapses and neurons and the neuromorphic hardware
- Well understand neuromorphic hardware development at device and circuit levels
