"Neural network framework using emerging technologies for screening Diabetic Retinopathy"
Masters graduate in Embedded Systems
Diabetic Retinopathy (DR) is one of the leading causes of permanent vision loss. Its current prevalence is around 45 millions across the globe and is projected to 70 million by 2045. Most of the people with this disease condition belong to remote and low income settings. We can reduce this incidence, if quality medical care is accessible in remote areas. With the current advancements in imaging technologies, fundus examination can be carried out on a handheld device. We need to improve such devices to deliver high quality services through auto detecting DR based on convolutional neural networks(CNNs) in an offline setting. Addressing these challenges, we aim to develop an integrated solution which delivers high compute at ultra-low power consumption. Firstly, we have created 3 datasets of different sizes merging multiple public datasets to create a vigilant model training process. This is to make the CNN model robust to real-world noise. CNNs trained on smaller datasets have shown a 15% accuracy drop on evaluation datasets where as CNNs trained on large datasets showed consistent performance. Secondly, we have proposed a new binary labelling scheme using multi-class output to maximize the utility of its softmax probabilities. We have achieved 90.26% accuracy on evaluation dataset with the new scheme. These high performing models along with compression techniques are implemented on resistive random access memory (RRAM) based computational in memory (CIM) architecture. These implementations resulted in atleast 200x improvement in energy consumption for inferring on one image when compared to CPU, GPU and mTPU (google coral dev board). Similarly, latency improvements 28x,130x and 2000x compared GPU, CPU and mTPU are registered.