Objectives
Our scientific and technological objectives can be summarized as follows:
- To study the theory, and develop algorithmic and architectural innovations for realizing adaptive and robust multi-timescale neural processing on mixed-signal analog/digital neuromorphic processors comprising both volatile and non-volatile memory devices to implement the synaptic circuits and TFT-based neurons.
- To develop novel hardware technologies that support on-chip learning with multiple time constants, both for synapses (volatile memory option combined with non-volatile memory, Electrochemical metallization, vacancy-type oxide-based memories, and Phase Change Memory), and neurons (TFT option exploration, plus integration with other devices).
- To study and develop an ultra-low-power, scalable and highly configurable neuromorphic computing processor capable of online, life-long learning for personalized neural learning and adaptation algorithms.
- To validate and demonstrate the project developments on realistic fully personalized edge application cases (by both simulation and board prototyping).
Workflow
![](https://memscales.eu/wp-content/uploads/2020/02/workflow-1024x770.png)
Target application domains
In addition to the earlier NeuRAM3 EU project analysis on the ECG healthcare analysis, also several other application studies have been performed, in 2 different major domains namely (1) autonomously navigating and moving like robots, drones and even cars and (2) sensor-based healthcare and life-style systems such as smart patches, smart wristbands, smart glasses and even smart shoes.
A table with summarizing requirements for time scales based on these studies is provided below.
Purpose Time scales
Immediate state/threats msec to second
Planning/monitoring across short periods seconds to minutes or hours (intra-day)
Tracking evolution over longer periods days to years
It can be concluded that we need to potentially span up to 9 orders of magnitude from milli-seconds up to years.