This paper studies the suitability of neuromorphic event-based vision cameras for spaceflight and the effects of neutron radiation on their performance. Neuromorphic event-based vision cameras are novel sensors that implement asynchronous, clockless data acquisition, providing information about the change in illuminance ≥120dB with sub-millisecond temporal precision. These sensors have huge potential for space applications as they provide an extremely sparse representation of visual dynamics while removing redundant information, thereby conforming to low-resource requirements. An event-based sensor was irradiated under wide-spectrum neutrons at Los Alamos Neutron Science Center and its effects were classified. Radiation-induced damage of the sensor under wide-spectrum neutrons was tested, as was the radiative effect on the signal-to-noise ratio of the output at different angles of incidence from the beam source. We found that the sensor had very fast recovery during radiation, showing high correlation of noise event bursts with respect to source macro-pulses. No statistically significant differences were observed between the number of events induced at different angles of incidence but significant differences were found in the spatial structure of noise events at different angles. The results show that event-based cameras are capable of functioning in a space-like, radiative environment with a signal-to-noise ratio of 3.355. They also show that radiation-induced noise does not affect event-level computation. Finally, we introduce the Event-based Radiation-Induced Noise Simulation Environment (Event-RINSE), a simulation environment based on the noise-modelling we conducted and capable of injecting the effects of radiation-induced noise from the collected data to any stream of events in order to ensure that developed code can operate in a radiative environment. To the best of our knowledge, this is the first time such analysis of neutron-induced noise has been performed on a neuromorphic vision sensor, and this study shows the advantage of using such sensors for space applications.

Journal Paper - IEEE Access, vol. 9, pp 85748-85763, 2021 IEEE
DOI: 10.1109/ACCESS.2021.3085136    ISSN: 2169-3536    » doi

Non-intrusive visual-based applications supporting the communication of people employing sign language for communication are always an open and attractive research field for the human action recognition community. Automatic sign language interpretation is a complex visual recognition task where motion across time distinguishes the sign being performed. In recent years, the development of robust and successful deep-learning techniques has been accompanied by the creation of a large number of databases. The availability of challenging datasets of Sign Language (SL) terms and phrases helps to push the research to develop new algorithms and methods to tackle their automatic recognition. This paper presents ‘SL-Animals-DV’, an event-based action dataset captured by a Dynamic Vision Sensor (DVS). The DVS records non-fluent signers performing a small set of isolated words derived from SL signs of various animals as a continuous spike flow at very low latency. This is especially suited for SL signs which are usually made at very high speeds. We benchmark the recognition performance on this data using three state-of-the-art Spiking Neural Networks (SNN) recognition systems. SNNs are naturally compatible to make use of the temporal information that is provided by the DVS where the information is encoded in the spike times. The dataset has about 1100 samples of 59 subjects performing 19 sign language signs in isolation at different scenarios, providing a challenging evaluation platform for this emerging technology.

Journal Paper - Pattern Analysis And Applications, vol. 24, no. 2, 2021 SPRINGER
DOI: 10.1007/s10044-021-01011-w    ISSN: 1433-7541    » doi

This paper presents a novel Look-up Table (LUT) calibration technique for static non-linearity compensation in analog-to-digital converters (ADCs) with digital redundancy, such as Successive Approximation Register (SAR), Algorithmic, Sub-ranging or Pipeline ADCs. The method compensates the performance limitations of the conventional LUT approach in presence of comparison noise and/or non-monotonicity. In these circumstances, the input-output transfer function of a redundant ADC becomes significantly multivalued - that is, different output codes can be achieved for the same input level at different time instants. This behavior is motivated because from sample to sample, in a design with redundancy, the processing signal path is not unique, causing that the error under calibration becomes time-dependent, something which is not contemplated in the conventional calibration model. To deal with this effect, this work proposes a digital low-cost post-processing of the standardized Integral-Non-linearity (INL), which resolves multivalued situations using a direct access to the internal redundant codes. The method improvements are validated by realistic SAR and Pipeline ADC case studies at behavioral level, and by experimental data from an 11-bit 60Msps Pipeline ADC implemented in a 130nm CMOS process. These experimental results show that the proposed calibration achieves an improvement of approximately 1.6 effective bits at full-scale input amplitude.

Journal Paper - IEEE Transactions on Circuits and Systems I-Regular Papers, vol. 68, no. 8, pp 3197-3210, 2021 IEEE
DOI: 10.1109/TCSI.2021.3066886    ISSN: 1549-8328     » doi

Oscillatory Neural Networks (ONNs) are currently arousing interest in the research community for their potential to implement very fast, ultra-low-power computing tasks by exploiting specific emerging technologies. From the architectural point of view, ONNs are based on the synchronization of oscillatory neurons in cognitive processing, as occurs in the human brain. As emerging technologies, VO₂ and memristive devices show promising potential for the efficient implementation of ONNs. Abundant literature is now becoming available pertaining to the study and building of ONNs based on VO₂ devices and resistive coupling, such as memristors. One drawback of direct resistive coupling is that physical resistances cannot be negative, but from the architectural and computational perspective this would be a powerful advantage when interconnecting weights in ONNs. Here we solve the problem by proposing a hardware implementation technique based on differential oscillatory neurons for ONNs (DONNs) with VO₂-based oscillators and memristor-bridge circuits. Each differential oscillatory neuron is made of a pair of VO₂ oscillators operating in anti-phase. This way, the neurons provide a pair of differential output signals in opposite phase. The memristor-bridge circuit is used as an adjustable coupling function that is compatible with differential structures and capable of providing both positive and negative weights. By combining differential oscillatory neurons and memristor-bridge circuits, we propose the hardware implementation of a fully connected differential ONN (DONN) and use it as an associative memory. The standard Hebbian rule is used for training, and the weights are then mapped to the memristor-bridge circuit through a proposed mapping rule. The paper also introduces some functional and hardware specifications to evaluate the design. Evaluation is performed by circuit-level electrical simulations and shows that the retrieval accuracy of the proposed design is comparable to that of classic Hopfield Neural Networks.

Journal Paper - Frontiers in Neuroscience, vol. 15, article 674567, 2021 FRONTIERS
DOI: 10.3389/fnins.2021.674567    ISSN: 1662-453X    » doi

In nowadays deeply scaled CMOS technologies, time-dependent variability effects have become important concerns for analog and digital circuit design. Transistor parameter shifts caused by Bias Temperature Instability and Random Telegraph Noise phenomena can lead to deviations of the circuit performance or even to its fatal failure. In this scenario extensive and accurate device characterization under several test conditions has become an unavoidable step towards trustworthy implementing the stochastic reliability models. In this paper, the statistical distributions of threshold voltage shifts in nanometric CMOS transistors will be studied at near threshold, nominal and accelerated aging conditions. Statistical modelling of RTN and BTI combined effects covering the full voltage range is presented. The results of this work suppose a complete modelling approach of BTI and RTN that can be applied in a wide range of voltages for reliability predictions.

Journal Paper - Solid-State Electronics, vol. 185, article 108112, 2021 ELSEVIER
DOI: 10.1016/j.sse.2021.108112    ISSN: 0038-1101    » doi