Incorporating artificial intelligence (AI) in electronic systems has been widely recognized as one of the key enablers for several emerging applications. However, the energy efficiency and the learning capabilities of state-of-the-art AI systems is far from that achievable by human brains. This research undertakes a cross-layer exploration spanning novel devices, low-power neural networks, and new learning schemes. The exploration will exploit Ferroelectric Field Effect Transistor (FeFET) technology with intrinsic neuro-mimetic features to achieve energy-efficient neural hardware and adaptable learning. The low-power hardware solutions and adaptive-learning algorithms have the potential to impact critical applications such as computer-aided diagnosis, robotics, speech/face recognition, and data classification, thereby directly benefiting areas such as healthcare, defense, security etc. Moreover, power savings should translate to longer battery life for edge devices and energy-efficient data processing for applications like wearable health-monitoring platforms. The project will leverage outreach programs at Purdue University and develop a summer Research Experiences for Undergraduates (REU) program to involve undergraduates and minority students in the project. The broad nature of this project will provide opportunity for undergraduate students to get introduced to the field of AI based on emerging technologies.
Spiking Neural Networks (SNNs), due to their self-learning capabilities, show promise in introducing adaptability in learning for AI systems, but suffer from low accuracy. Improving SNN accuracy and performance not only necessitates novel learning mechanisms that support adaptable lifelong learning, but also an intrinsically suitable technology for low-power scalable hardware. To address this critical need, this project will carry out a comprehensive devices-to-algorithms exploration of multi-domain FeFET based SNNs. The main objectives include (a) design of low-power neurons and synapses using FeFETs, and (b) development of adaptive and sequential learning algorithms utilizing the unique attributes of the neuro-mimetic devices. To enable bio-plausible features in FeFETs, physics-based device optimization will be carried out to utilize the multi-domain effects and domain dynamics of ferroelectrics. To facilitate cross-layer exploration, a devices-to-systems simulation framework will be developed capturing the rich dynamics of the neurons and synapses, their interactions in an SNN, and the impact of new learning algorithms on system performance/accuracy.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
