FPGA based hardware acceleration of sensor matrix

Abdul Mutaal Ahmad, Paul Lukowicz, Jingyuan Cheng

In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp-16) Pages 793-802 ACM 2016.


This paper describes the hardware acceleration of various feature calculation functions used in activity recognition. In this work we have used a large scale sensing matrix which recognizes and counts gym exercises. Human activity is played on pressure matrix and the sensor data is sent to computer using a wired protocol for further processing. The recorded data from matrix is huge making it impractical to process on a smart phone. We propose a FPGA (Field Programmable Gate Array) based processing methodology which not only accelerates sensing data processing but also reduces the size of 2D sensor data matrix to 10 features. The resultant feature set can be transferred using wireless medium to a smart phone or other processing unit where the classification can be done. Our system takes a matrix of arbitrary size and output a 'features' set for each matrix frame. We used HLS (High Level Synthesis), an approach to write algorithm for FPGA using SystemC/C/C++ instead of traditional VHDL/Verilog. Results show promising improvement in processing time as compared to Matlab. Since the size of data is reduced, wireless medium can be use to transmit data. Additionally, the development time for FPGA designs is greatly reduced due to the usage of an abstracted high level synthesis approach. This system is currently developed for pressure sensing system but this strategy can be applied to other sensing application like temperature sensor grid.

Weitere Links

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz