Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance

Spiking neural network (SNN) is interesting both theoretically and practically because of its strong bio-inspiration nature and potentially outstanding energy efficiency. Unfortunately, its development has fallen far behind the conventional deep neural network (DNN), mainly because of difficult training and lack of widely accepted hardware experiment platforms. In this paper, we show that a deep temporal-coded SNN can be trained easily and directly over the benchmark datasets CIFAR10 and ImageNet, with testing accuracy within 1% of the DNN of equivalent size and architecture. Training becomes similar to DNN thanks to the closed-form solution to the spiking waveform dynamics. Considering that SNNs should be implemented in practical neuromorphic hardwares, we train the deep SNN with weights quantized to 8, 4, 2 bits and with weights perturbed by random noise to demonstrate its robustness in practical applications. In addition, we develop a phase-domain signal processing circuit schematic to implement our spiking neuron with 90% gain of energy efficiency over existing work. This paper demonstrates that the temporal-coded deep SNN is feasible for applications with high performance and high energy efficient

DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks

Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are vulnerable to security threats, such as adversarial attacks, i.e., small perturbations added to the input for inducing a misclassification. Toward this, we propose DVS-Attacks, a set of stealthy yet efficient adversarial attack methodologies targeted to perturb the event sequences that compose the input of the SNNs. First, we show that noise filters for DVS can be used as defense mechanisms against adversarial attacks. Afterwards, we implement several attacks and test them in the presence of two types of noise filters for DVS cameras. The experimental results show that the filters can only partially defend the SNNs against our proposed DVS-Attacks. Using the best settings for the noise filters, our proposed Mask Filter-Aware Dash Attack reduces the accuracy by more than 20% on the DVS-Gesture dataset and by more than 65% on the MNIST dataset, compared to the original clean frames. The source code of all the proposed DVS-Attacks and noise filters is released at https://github.com/albertomarchisio/DVS-Attacks

DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks

Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are vulnerable to security threats, such as adversarial attacks, i.e., small perturbations added to the input for inducing a misclassification. Toward this, we propose DVS-Attacks, a set of stealthy yet efficient adversarial attack methodologies targeted to perturb the event sequences that compose the input of the SNNs. First, we show that noise filters for DVS can be used as defense mechanisms against adversarial attacks. Afterwards, we implement several attacks and test them in the presence of two types of noise filters for DVS cameras. The experimental results show that the filters can only partially defend the SNNs against our proposed DVS-Attacks. Using the best settings for the noise filters, our proposed Mask Filter-Aware Dash Attack reduces the accuracy by more than 20% on the DVS-Gesture dataset and by more than 65% on the MNIST dataset, compared to the original clean frames. The source code of all the proposed DVS-Attacks and noise filters is released at https://github.com/albertomarchisio/DVS-Attacks.Comment: Accepted for publication at IJCNN 202

The Effect of Active Learning on Viewpoint Dependence for Novel Objects

Active learning of novel objects can facilitate subsequent object recognition and discrimination, but the reasons for its beneficial effects remain unclear. One potential explanation is that active learning enables the formation of a more detailed, realistic, or useful neural object representation than does passive learning. The current study addressed the question of whether active vs. passive learning of objects affects viewpoint discrimination. Participants learned novel wire-like objects either actively or passively and then completed a psychophysical task which they discriminated object orientation. This study did not find a significant difference in viewpoint discrimination between actively and passively learned object representations, which stands in contrast to earlier studies that found an effect of active learning on object recognition across different viewpoints. This suggests that viewpoint discrimination and viewpoint generalization rely on different mechanisms

Radioisotope identification with neuromorphic methodology: different solutions and evaluations

Early detection of radioisotopes plays an increasingly important role in the modern world. It allows the possibility of quick countermeasures when faced with potentially hazardous radioactive materials like dirty bombs, and nuclear leakage. This could secure the lives of the innocent in populated areas including airports, stadiums or ports. A light-weight compact handheld device could be used in this situation for the patrol team. However, the operating hours for these devices are normally constrained by the batteries they carry. More efficient al- gorithms or solutions are needed for this resource-constraint application to extend the battery life so that security patrol is not frequently interrupted by the recharge. Event-based processing is a novel technique that allows the computing unit to operate only when there is a key event while staying idle otherwise. Spiking neural network (SNN) is a promising candidate for event-based processing and also known as neuromorphic method- ology due to the biomimicry plausibility, which could be easily implemented and still offer comparable accuracy to its counterpart — artificial neural network (ANN), which is notoriously power-hungry. In this research work, it will be demonstrated that using SNN for radioisotope identification (RIID) is possible and capable of achieving the same or even better accuracy when compared with ANNs. Meanwhile, the power consumption of the proposed method on a field program- mable gate array (FPGA) shows that power reduction is highly significant compared with the old software implementation on a smartphone. The task has been delivered in two parts, we first attempted an unsupervised Spike-Timing- Dependent Plasticity (STDP) SNN implementation on SpiNNaker, an emulation platform for SNN. This demonstrates the capability of classifying radioisotopes using purely SNN compat- ible training methods and architecture. We then managed to implement a more complex bin-ratio ensemble SNN (BESNN) on FPGA with better performance. To achieve this implementation, a new SNN conversion method was created to facilitate the digital hardware implementation. This conversion flow allows the highly sparse weight matrix representation without sacrificing overall accuracy. In the meantime, the power consumption of the mentioned design has been characterised, which could be used to estimate the battery life of a handheld system while functioning. Even though this design has been validated on an FPGA, further squeeze for the power saving is possible if an application specific integrated circuits (ASIC) could be delivered. Furthermore, the analogue unit used in the design is a compromise given that the logarithm could not be done by a spiking neuron at the moment. This prevents an end-to-end application, which is preferred for higher integration and potentially more power conservation. According to our knowledge, applying neuromorphic methodology to address RIID represents uncharted territory, especially in the context of power characterisation, an aspect that has not been explored previously. This research work fills the gap that is present in the research field and also offers a functional low-power prototype for the handheld RIID device producer. This project pioneers the use of an event-based processing algorithm for radioisotope identi- fication, marking a significant advancement in the field. Leveraging Spiking Neural Networks (SNNs) on specialised hardware, the project establishes a comprehensive application flow, showcasing the efficacy and potential of SNNs in this domain. The implementation of an unsupervised STDP algorithm for radioisotope identification is also groundbreaking, introducing a local self-learning rule for complex tasks beyond handwritten digit recognition. Additionally, the bin-ratio ensemble project achieves remarkable accuracy, setting new bench- marks in the field. It represents the first ensemble SNN application in radioisotope identifica- tion, further enhanced by an innovative ANN-SNN conversion method with iterative pruning to reduce computational overhead. Furthermore, this research provides detailed insights into sparse SNN construction and char- acterises hardware implementation, shedding light on power and energy consumption con- siderations