ABSTRACT

The goal of this project is to develop an energy-efficient and smart extreme edge device by taking inspiration from the human brain. Extreme edge devices can enable a variety of applications. Specifically, the devices can be used for remote tracking of rapid changes in the arctic, autonomous navigation of aerial and ground robots for deep space exploration, and monitoring and securing critical infrastructure. However, the current technology requires remotely sensed data to be processed in the cloud. This framework has several drawbacks, such as the delay between sensing and decision, shorter battery life due to high power consumption, and privacy concerns related to data transfer. The hardware developed as part of this proposal will seek to address the previously noted challenges and will advance the state-of-the-art in extreme edge devices. Specifically, the grant will enable the development of a reconfigurable brain-inspired processor with the capacity to learn based on input data.
The hardware developed as part of this proposal will be used to train and motivate the next generation of students in the areas of microelectronics. Further, the hardware developed will use open-source computer aided design tools to enable broader dissemination of the developed technology.


The study proposes to develop a reconfigurable mixed-signal neuromorphic hardware for processing data at the extreme edge. This proposal aims to increase the energy efficiency of neuromorphic hardware by employing mixed-signal circuits to model the neurons and synapses. Further, we propose to incorporate programmable mixed-signal circuit topologies and explore techniques to co-optimize the hardware and software models to learn and adapt the network parameters in the presence of mismatch and variations. In addition, the proposal will also explore circuit topologies to perform learning on-chip. Based on these individual elements, the proposal will investigate a system architecture to develop reconfigurable hardware that can compile a spiking neural network with the capability to learn on-chip for performing extreme edge tasks. The extreme edge task we plan to validate the developed hardware is object detection using openly available datasets and a quadcopter with a limited battery capacity to perform object detection. The proposal will address the knowledge gaps in designing a learning algorithm for a mixed-signal spiking neural network with variation and mismatch, circuit topologies and system architecture for performing on-chip learning with limited resources, and advance the state-of-the-art in energy-efficient neuromorphic hardware.

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.

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