The technologies designed in MeM-Scales target the IoT / edge computing markets, which is expected to grow sharply in the coming years. The market for edge computing chipsets is projected to surpass 50 billion USD by 2025; in particular, the market is projected to outstrip cloud computing chipsets (e.g. devices based in data centers) by a factor of three.
MeM-Scales is focused on event-based or spiking neural network (SNN) or spiking neuromorphic applications where a diverse range of time scales is present and required. So, we mostly aim at streaming applications where the input/sensor data is sampled in a near-continuous stream and information of the past has to be stored across multiple time horizons. One major application domain where this is valid is in autonomous navigation and moving vehicles such as robots, drones and even cars. In this-case, one can take advantage of heterogeneous collection of video cameras, radar sensors and potentially also lidars. Another major application target domain are sensor-based healthcare and life-style systems such as smart patches, smart wristbands, smart glasses and even smart shoes. Also in that case, we can make use of sensory fusion by combining a heterogeneous set of sensors for collecting information such as ECG, EMG, bio-impedance streams and potentially also brain signals through EEG sensors and neuro-probes.
Major progress beyond the state of the art is expected by merging all the innovations at the levels of algorithms, non-volatile / volatile devices, neuromorphic circuit designs, and scalable CMOS and TFT connection schemes in a cross-disciplinary effort towards the realization of multi-time scale spiking neural processing systems. MeM-Scales targets at deliver ultra-low power autonomous life-long on-line learning systems which will have a tremendous impact on the above mentioned edge computing applications domains.
