The NeuRAM3 project aims at defining a technology that will match the new requirements for neuromorphic (i.e. inspired to the biological mechanisms and architectures of the brain) data processing, in particular addressing the issues of power consumption and power efficiency.
The overall aim is to provide a European solution to deploy Artificial Intelligence (AI) techniques much closer to where the analysed data originates and to where necessary actions need to be executed. Today most of the AI is concentrated in the large data centers (the "Cloud") where both large amount of data can be stored and powerful High Performance Computing (HPC) tools can operate on them. However, this approach has several shortcomings for a number of applications. The first is the energy consumption necessary to transfer and store all the data, the second is its centralised nature that poses problems of security and confidentiality on the use of the data, the third is the fact that transmission times are not compatible with the need of real time action in some application. In order to overcome these issues, we propose to use smaller systems, closer to the data generation or even embedded with the sensors, that would be capable of extracting locally and in real time some information from the data and so streamline the analysis and decision process. Moreover we investigated the possibility of integrating also some degree of learning in these systems so that they can evolve adapting to the specificity of the environment where they are deployed.
To achieve this goal an interdisciplinary group of partners formed the consortium combining specialists of theory of computing, neuromorphic architecture design, nano-technology, and 3D VLSI integration. The idea was to co-develop novel devices, advanced fabrication technologies, circuit design, computing architectures and dedicated algorithms in order to achieve the best compromise possible between performance and power. In particular the objective is to have a scalable solution that can address both the needs of interfacing with low level sensors signals but also interconnected in multichip systems to achieve complex data analysis.
By the end of the project, the consortium has realised a number of chips which prove how Spiking Neural Networks can be successfully used in real life applications (like real time continuous ECG monitoring), how compact and easily deployable algorithms like variants of Reservoir Computing are sufficient for a number of cases, how this network can benefit from new technologies like FDSOI and RRAMs, how they can be scaled up by segmented buses using TFTs, how RRAM can be useful for both conventional digital Neural Networks and new mixed analog/digital architectures, and how a denser 3D technology including RRAMs is manufacturable for the future needs of circuit implementations.
