Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, computational memory architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we experimentally demonstrate for the first time, the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize audio signals of alphabets encoded using spikes in the time domain and to generate spike trains at precise time instances to represent the pixel intensities of their corresponding images. Moreover, with a statistical model capturing the experimental behavior of the devices, we investigate architectural and systems-level solutions for improving the training and inference performance of our computational memory-based system. Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

An Event-Driven Multi-Kernel Convolution Processor Module for Event-Driven Vision Sensors

Event-Driven vision sensing is a new way of sensing visual reality in a frame-free manner. This is, the vision sensor (camera) is not capturing a sequence of still frames, as in conventional video and computer vision systems. In Event-Driven sensors each pixel autonomously and asynchronously decides when to send its address out. This way, the sensor output is a continuous stream of address events representing reality dynamically continuously and without constraining to frames. In this paper we present an Event-Driven Convolution Module for computing 2D convolutions on such event streams. The Convolution Module has been designed to assemble many of them for building modular and hierarchical Convolutional Neural Networks for robust shape and pose invariant object recognition. The Convolution Module has multi-kernel capability. This is, it will select the convolution kernel depending on the origin of the event. A proof-of-concept test prototype has been fabricated in a 0.35 m CMOS process and extensive experimental results are provided. The Convolution Processor has also been combined with an Event-Driven Dynamic Vision Sensor (DVS) for high-speed recognition examples. The chip can discriminate propellers rotating at 2 k revolutions per second, detect symbols on a 52 card deck when browsing all cards in 410 ms, or detect and follow the center of a phosphor oscilloscope trace rotating at 5 KHz.Unión Europea 216777 (NABAB)Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

High speed event-based visual processing in the presence of noise

Standard machine vision approaches are challenged in applications where large amounts of noisy temporal data must be processed in real-time. This work aims to develop neuromorphic event-based processing systems for such challenging, high-noise environments. The novel event-based application-focused algorithms developed are primarily designed for implementation in digital neuromorphic hardware with a focus on noise robustness, ease of implementation, operationally useful ancillary signals and processing speed in embedded systems

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