ML

Our research focuses on memory-based deep learning techniques, including SRAM/ReRAM-based deep learning processing-in-memory (PIM) and Flash memory based robust deep learning inference techniques.

To maximize the performance and efficiency of deep learning based data processing, we are currently exploring sensor platform design optimized for deep learning by leveraging the interactions between a sensor platform and a DNN.

Deep neural networks (DNNs) are widely adopted at the IoT edge devices to enable more intelligence. However, the biggest challenge is their storage demand and computational complexity. As DNNs contain a large number of synaptic weights, the memory demand is a key challenge for application of DNNs, especially for memory-constrained platforms such as mobile systems.

Based on the trade-off study between energy, accuracy, and throughput of an edge platform with an embedded DNN, we are currently exploring collaborative DNN inference between the edge and the host to optimally utilize the available bandwidth and energy.

One of the challenges of deploying deep neural networks in sensor platforms is the variations in the input images; structural noise (adversarial images), inherent random noise (image perturbation), input scene variability, and weight value errors.