Low power DNN Processor for Embedded AI in IoT sensors
Deep neural networks (DNNs), which are loosely modelled after the human brain, have been shown to be
remarkably successful in recognizing and interpreting patterns in many applications, such as image and video
processing. However, due to high computational complexity, power, and resource requirements, DNNs are not
well explored for low power applications such as Internet of Things (IoT) sensors, wearable healthcare, etc.
IoT devices have stringent energy and resource constraints and, in many cases, deal with one-dimensional
time series data.
This project will investigate DNN hardware accelerator architectures for low-power edge implementation. The
project will address the challenges of implementing sparse DNN models in energy-efficient IoT edge sensors.
The DNN accelerator hardware developed will be based on a RISC-V CPU and will be scalable,
programmable, and easily adaptable to different DNN model types. The accelerator developed will be
demonstrated in an FPGA (or ASIC) for a wearable biomedical application such as heartbeat classification