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Neuromorphic computing systems are an emerging field that takes its inspiration from the biological neural architectures and computations inside the mammalian nervous system. The spiking neural networks (SNNs) mimic real biological neural networks by conveying information through the communication of short pulses between neurons. Since each neuron in these networks is connected to thousands of others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, very low communication latency is also necessary. On the other hand, the combination of Two-dimensional Networks-on-Chip (2D-NoC) and Three-dimensional Integrated Circuits (3D-ICs) can provide a scalable interconnection fabric in large-scale parallel SNN systems. Although the SNNs have some intrinsic fault-tolerance properties, they are still susceptible to a significant amount of faults; especially, when we talk about integrating the large-scale SNN models in hardware. Consequently, the need for efficient solutions capable of avoiding any malfunctions or inaccuracies, as well as early fault-tolerance assessment, is becoming increasingly necessary for the design of future large-scale reliable neuromorphic systems. This paper first presents an analytical model to assess the effect of faulty connections on the performance of a 3D-NoC-based spiking neural network under different neural network topologies. Second, we present a fault-tolerant shortest-path k-means-based multicast routing algorithm (FTSP-KMCR) and architecture for spike routing in 3D-NoC of spiking neurons (3DFT-SNN). Evaluation results show that the proposed SP-KMCR algorithm reduces the average latency by 12.2% when compared to the previously proposed algorithm. In addition, the proposed fault-tolerant methodology enables the system to sustain correct traffic communication with a fault rate up to 20%, while only suffering 16.23% longer latency and 5.49% extra area cost when compared to the baseline architecture.INDEX TERMS Spiking neural networks, performance assessment, fault-tolerant, k-means based multicast routing, scalable architecture.

Neuromorphic systems have shown improvements over the years, leveraging Spiking neural networks (SNN) event-driven nature to demonstrate low power consumption. As neuromorphic systems require high integration to form a functional silicon brain-like, moving to 3D integrated circuits (3D-ICs) with three-dimensional network on chip (3D-NoC) interconnect is a suitable approach that allows scalable design, shorter connections, and lower power consumption. However, highly dense neuromorphic systems also encounter the reliability issue where a single point of failure can affect the systems'operation. Because neuromorphic systems rely heavily on spike communication, an interruption or violation in the timing of spike communication can adversely affect the performance and accuracy of a neuromorphic system. This paper presents NASH, a a fault-tolerant 3D-NoC based neuromorphic system that incorporates as processing elements, lightweight spiking neuron processing cores (SNPCs) with spike-timing-dependent-plasticity (STDP) on-chip learning. Each SNPC houses 256 leaky integrate-and-fire (LIF) neurons and 65k synapses. Evaluation results on MNIST classification, using the fault-tolerant shortest-path K-means-based multicast routing algorithm (FTSP-KMCR), show that the NASH system can maintain high accuracy for up to 30% permanent fault in the interconnect with an acceptable area and power overheads when compared to other existing systems.

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Autonomous Driving has recently become a research trend and efficient autonomous driving system is difficult to achieve due to safety concerns, Applying traffic light recognition to autonomous driving system is one of the factors to prevent accidents that occur as a result of traffic light violation. To realize safe autonomous driving system, we propose in this work a design and optimization of a traffic light detection system based on deep neural network. We designed a lightweight convolution neural network with parameters less than 10000 and implemented in software. We achieved 98.3% inference accuracy with 2.5 fps response time. Also we optimized the input image pixel values with normalization and optimized convolution layer with pipeline on FPGA with 5% resource consumption.

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The use of robotic arms in various fields of human endeavor has increased over the years, and with recent advancements in artificial intelligence enabled by deep learning, they are increasingly being employed in medical applications like assistive robots for paralyzed patients with neurological disorders, welfare robots for the elderly, and prosthesis for amputees. However, robot arms tailored towards such applications are resource-constrained. As a result, deep learning with conventional artificial neural network (ANN) which is often run on GPU with high computational complexity and high power consumption cannot be handled by them. Neuromorphic processors, on the other hand, leverage spiking neural network (SNN) which has been shown to be less computationally complex and consume less power, making them suitable for such applications. Also, most robot arms unlike living agents that combine different sensory data to accurately perform a complex task, use uni-modal data which affects their accuracy. Conversely, multi-modal sensory data has been demonstrated to reach high accuracy and can be employed to achieve high accuracy in such robot arms. This paper presents the study of a multi-modal neurorobotic prosthetic arm control system based on recurrent spiking neural network. The robot arm control system uses multi-modal sensory data from visual (camera) and electromyography sensors, together with spike-based data processing on our previously proposed R-NASH neuromorphic processor to achieve robust accurate control of a robot arm with low power. The evaluation result using both uni-modal and multi-modal input data show that the multi-modal input achieves a more robust performance at 87%, compared to the uni-modal.