Nowadays, ~220M of the worldwide population of which over 50M only in EU, currently live with more than one chronic condition. Early and accurate detection of adverse events are fundamental for proper clinical management. To meet this need, the healthcare system is becoming increasingly personalized with the help of self-health monitoring systems and in particular medical wearable devices. However, constant monitoring generates large amounts of raw data which must be stored and processed to extract pathological biomarkers. Normally, this is done using microprocessors commonly found in computers as central processing units (CPUs) or graphic processing units (GPUs), or on specialized microprocessors such as digital signal processors (DSPs) that have some limitations as implying high power consumption (>100W) and no real time signal processing. aiCTX offers DynapIP, a neuromorphic chip with extremely long battery life (3 months) and low-power consumption (<100uW) able to perform sophisticated signal processing and react to sensory input in real-time enabling continuous monitoring and real time analysis of biological signals captured by wearable medical devices. The hardware can be fully customized based on client needs and these approaches can be applied to a wide variety of biosensors, such as ECG, EOG, EMG and EEG. Wearable devices using DynapIP automatically identify in real-time (less than 100ms) pathological events with high accuracy facilitating faster and immediate diagnosis for healthcare personnel. During the feasibility assessment, a go-to-market strategy will be established, as well as further development plan will be drafted. During the innovation project, aiCTX will optimize the existing neuron/synapses hardware modules for different biosensors and the software integration to support the new hardware modules of the DynapIP. Pilot study for validation of biosignal analysis and functionality of the technology will be conducted together with secured partners

The brain, with its remarkable computational properties, provides animals with capabilities of physical autonomy, interaction and adaptation that are unmatched by any artificial system. The brain is a complex network that has evolved to optimize processing of real-world inputs by relying on event-based signaling and self-reorganizing connectivity. Spikes (the events) are transmitted between neurons through synapses which undergo continuous ‘birth’-‘death’ and adjustment, reconfiguring brain circuits and adapting processing to ever changing inputs. The scientific and technological objective of the project is to create a hybrid system where a neural network in the brain of a living animal (BNN) and a silicon neural network of spiking neurons on a chip (SNN) are interconnected by neuromorphic synapses, thus enabling co-evolution of connectivity and co-processing of information of the two networks

The project MeM-Scales aims at lifting neuromorphic computing in analog spiking microprocessors to an entirely new level of performance. Work in this project is based on a dedicated commitment that novel hardware and novel computational concepts must be co-evolved in a close interaction between nano-electronic device engineering, circuit and microprocessor design, fabrication technology and computing science (machine learning and nonlinear modeling). A key to reflecting "hardware physics" in "computational function" and vice versa is the fundamental role played by multiple timescales. Here MeM-Scales introduces a number of innovations. On the side of physical substrates, novel memory and device technologies, supporting on-chip learning over multiple timescales for both synapses and neurons, will be fabricated. To enable timescales spanning up to 9 (!) orders of magnitude both volatile memory and non-volatile memory as well as Thin Film Transistor technology will be exploited. On the side of computational theory, autonomous learning algorithms and architectures supporting computation over these wide range of timescales will be developed. These computational methods are specifically tailored to cope with the low numerical precision, parameter drift, stochasticity, and device mismatch which are inherent in analog nano-scale devices. These cross-disciplinary efforts will lead to the fabrication of an innovative hardware/software platform as a basis for future products which combine extreme power efficiency with robust cognitive computing capabilities. This new kind of computing technology will open new perspectives, for instance, for high-dimensional distributed environmental monitoring, implantable medical diagnostic microchips, wearable electronics or human-computer interfacing.

Massive adoption of computing in all aspects of human activity has led to unprecedented growth in the amount of data generated. Machine learning has been employed to classify and infer patterns from this abundance of raw data, at various levels of abstraction. Among the algorithms used, brain-inspired, or “neuromorphic”, computation provides a wide range of classification and/or prediction tools. Additionally, certain implementations come about with a significant promise of energy efficiency: highly optimized Deep Neural Network (DNN) engines, ranging up to the efficiency promise of exploratory Spiking Neural Networks (SNN). Given the slowdown of silicon-only scaling, it is important to extend the roadmap of neuromorphic implementations by leveraging fitting technology innovations. Along these lines, the current project aims to sweep technology options, covering emerging memories and 3D integration, and attempt to pair them with contemporary (DNN) and exploratory (SNN) neuromorphic computing paradigms. The process- and design-compatibility of each technology option will be assessed with respect to established integration practices. Core computational kernels of such DNN/SNN algorithms (e.g. dot-product/integrate-and-fire engines) will be reduced to practice in representative demonstrators.

The fundamental goal of the ANDANTE project is to leverage innovative hardware platforms to build strong hardware / software platforms for artificial neural networks (ANN) and spiking neural networks (SNN) as a basis for future products in the Edge IoT domain, combining extreme power efficiency with robust neuromorphic computing capabilities and demonstrate them in key application areas. The main objective of ANDANTE is to build and expand the European eco-system around the definition, development, production and application of neuromorphic hardware through an efficient cross-fertilization between major European foundries, chip design, system houses, application companies and research partners, as presented by the European Leader Group (ELG). The project brings together world class expertise to bring the world class expertise and infrastructures of Imec, CEA and FhG together with semiconductor companies, fabless, system houses, SMEs and application experts to explore and demonstrate the capabilities provided by the developed technologies. In the project, several applications will be assessed in key domains where Europe is strong (automotive, digital farming, digital industry, mobility and digital life). The aim is to reinforce and maintain strong leadership in these areas by bringing industry in contact with future memory technologies at a low TRL level (MRAM, OXRAM, FeFET). These cross-disciplinary efforts will lead to development of innovative hardware / software deep learning solutions, based on high TRL level RRAM/PCM and FeFET, to enable future products which combine extreme power efficiency with robust cognitive computing capabilities. This new kind of computing technology, combining ANN and SNN capabilities, will open new perspectives, for instance, environmental monitoring, and wearable electronics.