Despite the highly promising advances in Machine Learning (ML) and Deep Learning (DL) in recent years, DL requires significant hardware acceleration to be effective, as it is rather computationally expensive. Moreover, miniaturisation of electronic devices requires small form-factor processing units, with reduced SWaP (Size,Weight and Power) profile. Therefore, a completely new processing paradigm is needed to address both issues. In this context, the concept of neuromorphic (NM) engineering provides an attractive alternative, seen as the analog/digital implementation of biologically brain inspired neural networks. NM systems propagate spikes as means of processing data, with the information being encoded in the timing and rate of spikes generated by each neuron of a so-called spiking neural network (SNN). Based on this, the key advantages of SNNs are: less computational power required, more efficient and faster processing, much lower power consumption. This paper reports on the current state of the art in the field of NM systems, and it describes three application scenarios of SNN-based processing for security and defence, namely target detection and tracking, semantic segmentation, and control.
A new approach for imaging that is solely based on the time of flight of photons coming from the entire imaged scene, combined with a novel machine learning algorithm for image reconstruction: a spiking convolutional neural network (SCNN) named Spike-SPI (Spiking - Single Pixel Imager). The approach uses a single point detector and the corresponding time-counting electronics, which provide the arrival time of photons in the form of spikes distributed over time. This data is transformed into a temporal histogram containing the number of photons per arrival time. A SCNN that converts the 1D temporal histograms into a 3D image (2D image with depth map) by exploiting the feature extraction capabilities of convolutional neural networks (CNNs), the high dimensional compressed latent space representations of a variational encoder-decoder network structure, and the asynchronous processing capabilities of a spiking neural network (SNN). The performance of the proposed SCNN is analysed to demonstrate the state-of-the-art feature extraction capabilities of CNNs and the low latency asynchronous processing of SNNs that offer both higher throughput and higher accuracy in image reconstruction from the ToF data, when compared to standard ANNs. The results of Spike-SPI show an increase in spatial accuracy of 15% over then ANN, using the Intersection of Union (IoU) for the objects in the scene. While also delivering a 100% increase over then ANN in object reconstruction signal to noise ratio (RSNR) from ~3dB to ~6dB. These results are also consistent across a range of IRF (Instrument Response Functions) values and photo counts, highlighting the robust nature of the new network structure. Moreover, the asynchronous processing nature of the spiking neurons allow for a faster throughput and less computational overhead, benefiting from the operational sparsity in the single point sensor.
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