Approximate Computing-Based Processing of MEA Signals on FPGA

Microelectrode arrays (MEAs) are essential equipment in neuroscience for studying the nervous system's behavior and organization. MEAs are arrays of parallel electrodes that work by sensing the extracellular potential of neurons in their proximity. Processing the data streams acquired from MEAs is a computationally intensive task requiring parallelization. It is performed using complex signal processing algorithms and architectural templates. In this paper, we propose using approximate computing-based algorithms on Field Programmable Gate Arrays (FPGAs), which can be very useful in custom implementations for processing neural signals acquired from MEAs. The motivation is to provide better performance gains in the system area, power consumption, and latency associated with real-time processing at the cost of reduced output accuracy within certain bounds. Three types of approximate adders are explored in different configurations to develop the signal processing algorithms. The algorithms are used to build approximate processing systems on FPGA and then compare them with the accurate system. All accurate and approximate systems are tested on real biological signals with the same settings. Results show an enhancement in processing speed of up to 37.6% in some approximate systems without a loss in accuracy. In other approximate systems, the area reduction is up to 14.3%. Other systems show the trade between processing speed, accuracy, and area.