We propose to work towards the development of analog-to-digital converted tailored for neuromorphic processing systems. This novel type of analog-to-digital converter will be compatible with standard Address Event Representation scheme that is currently used in state-of-the art neuromorphic event-based systems. The ADC will have to perform data compression in the analog domain and near the sensor.
Analog-digital-conversion circuits (ADC) are the core of every integrated circuit (IC) that interfaces with sensory inputs or other analog information processing elements. The performance of the ACD block is crucial in guaranteeing a high fidelity of the signal processing carried out by the following computational stages.
Neuromorphic computing systems exploits digital pulses for both computation and communication, it is therefore crucial to develop novel ADCs that can directly interface with these new class of massively parallel processing units.
In this project we propose to follow a novel approach for analog-to-digital conversion: we propose to do develop an analog-to-digital converter system that can be directly interfaced to neuromorphic spiking neural processing circuits. This novel ADC will enable data compression by directly computing on the large amount of data available in the analog domain, resulting in the transmission of only the low-bandwidth outcome of the preprocessing. The output of the ADC will be a pulse of digital events (spikes) that will be sent to remote neuromorphic computing or actuating modules.
This project can result, depending on student performance, with the tape-out of the designed circuits, part of the work will also be spent in carrying out testing and characterization of the ADC. The outcome of the project is to be published in high impact journals and might as well be patented.
 Corradi, Federico, and Giacomo Indiveri. "A neuromorphic event-based neural recording system for smart brain-machine-interfaces." IEEE transactions on biomedical circuits and systems 9.5 (2015): 699-709.
 Mayr, Christian G., et al. "Configurable analog-digital conversion using the neural engineering framework." Frontiers in neuroscience 8 (2014): 201.
 Tapson, Jonathan, and André van Schaik. "An asynchronous parallel neuromorphic ADC architecture." Circuits and Systems (ISCAS), 2012 IEEE International Symposium on. IEEE, 2012.
 Yang, Minhao, Shih-Chii Liu, and Tobi Delbruck. "A dynamic vision sensor with 1% temporal contrast sensitivity and in-pixel asynchronous delta modulator for event encoding." IEEE Journal of Solid-State Circuits 50.9 (2015): 2149-2160.
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