Sketching-based high-performance biomedical big data processing accelerator

Amey M. Kulkarni, Ali Jafari, Chris Sagedy, Tinoosh Mohsenin

Abstract: Multi-Sensor health monitoring systems are used to predict near future events of our health system. Each sensor generates humongous amount of data per second and needs to be processed in real-time. At the same time health monitoring systems are battery operated, thus they have rigid constraints on power and area of processing platform. Additionally, health monitoring systems should be accurate, thus we adapt machine learning techniques to improve detection accuracy. We propose a programmable Big Data Processing framework to reduce on-chip communications and computations, thus reducing energy of the processing. We integrate a low-overhead sketching framework with a low-power programmable PENC many-core platform. The sketching technique reduces the data communications and computations, additionally processing time is scaled down by parallel processing on the many-core platform. For demonstration we show seizure detection application with 22-channel of electroencephalograph (EEG), each channel generates 256 samples per second requiring total of 88 Kbps data rate. The computations are reduced by 16× while energy consumption of processing is reduced up to 68%. For compression rates of 2-16×, the seizure detection performance for sensitivity and specificity is degraded by 2.07% and 2.97%, respectively for Logistic Regression classifier.