This project focuses on the development of neural prostheses or brain implants to advance the scientific community's understanding of how the brain works, and to take a step towards devising treatment for neurological disorders. Brain implants are devices that are surgically embedded under the skull (of animals or humans in the context of scientific experiments and treatment of neurological disorders respectively) and placed on brain tissue, where they stimulate and record from hundreds of neurons. These devices are being used today to record neuronal electro-physiological data to unlock mysteries of the brain; to treat symptoms of Parkinson's disease, Tourette's syndrome, and epilepsy, with techniques like deep brain stimulation; and to offer treatment to those afflicted by paralysis or spinal cord damage via motor cortex implants. A key design issue with brain implants is that they are highly energy constrained, because they are embedded under the skull, and techniques like wireless power can heat up the brain tissue surrounding the implant. This project offers architectural techniques to lower the power consumption and energy usage of processing elements integrated on brain implants, whether they are general-purpose processors, customized integrated circuits, or programmable hardware. In tandem with its scientific studies, this project integrates an educational component to train high-school students, undergraduates, and PhD students on neuro-engineering techniques crucial to the society's continued efforts to shed light on how the brain works.
In terms of technical details, this project performs the first study on architectural techniques to improve the energy efficiency of embedded processors on implants by leveraging their existing low-power modes. Low-power modes can be used in the absence of interesting neuronal activity, which corresponds to periods of time when the implant is not performing useful work and the processor can be slowed down. A critical theme of this project is to show that hardware traditionally used to predict program behavior (e.g., branches or cache reuse) can also be co-opted to also predict brain activity, and hence anticipate interesting/non-interesting neuronal spiking. Such predictors can consequently be used to drive the implant processor in and out of low power mode. This project studies how to design hardware brain activity predictors that predict neuronal activity accurately, scalably, and efficiently, and how to integrate such predictors with low power modes on commodity embedded processors. The techniques are drawn from hardware machine-learning approaches for program prediction and consider neuronal spiking data extracted from brain sites on mice, sheep, and monkeys. Successful deployment of these approaches is expected to save as much as 85% of processor energy, effectively quadrupling battery lifetimes on implants being designed for mice.
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.
