Circuits and Systems for a Spiking Neuromorphic Network in 28 nm CMOS

Neuromorphic computing can be used to efficiently implement spiking neural networks.<br/>Such spiking neural networks can be used in edge AI applications, where low power consumption is paramount.<br/>The use of analog components allows for extremely low power implementations.<br/>This thesis contributes the designs of an analog spike generator,...

Modeling of router structure for SNN-applicable NoC definitions

Spiking neural networks (SNN), as the third-generation artificial neural network, has a similar potential pulse triggering mechanism to the biological neuron. This mechanism enables the spiking neural network to increase computing power compared to the traditional artificial neural network to process complex information. However, a large number...

Designing Asynchronous Gate Library with new System Level Trade-offs

This dissertation describes an approach to building a self-timed asynchronous pulse-mode serial link circuit. Unlike asynchronous handshake circuits or synchronous circuits, this design style does not require any feedback control blocks, which can increase latency, or any clock recovery circuits, which can increase energy consumption and...

Efficient implementation of an audio preprocessing algorithm for SNN keyword spotting

Mobile devices are getting increasingly powerful, becoming compatible<br/>for an ever increasing set of functionality. Applications based around<br/>neural networks however still have to offload parts of their computations<br/>to the cloud since current Artificial Neural Networks (ANNs) are<br/>still too computationally expensive for any...

Physical Characterization of Asynchronous Logic Library: A Design of AER Transmitter and Its Characterization and Back-end Design Flow

Neuromorphic electronic systems have used asynchronous logic combined with continuous-time analog circuits to emulate neurons, synapses, and learning algorithms. It is attractive because of its low power consumption and feasible implementation. Typically, the neuron firing rates are lower than the modern digital systems. Thus, the endpoints of...

Sound-recognition using Spiking Neural Networks

The development of the Spiking Neural Network (SNN) offers great potential in combination with new types of event-based sensors, by exploiting the embedded temporal information. When combined with dedicated neuromorphic hardware it enables ultra-low power solutions and local on-chip learning. This work implements and presents a viable...

Population Step Forward Encoding Algorithm: Improving the signal encoding accuracy and efficiency of spike encoding algorithms

Conversion from digital information to spike trains is needed for Spiking Neural Networks. Moreover, it is one of the most important steps for Spiking Neural Networks. This conversion could lead to much information loss depending on which encoding algorithm is used. Another major problem that can occur in a specific use-case is the limited...

A robust modular spiking neural networks training methodology for time-series datasets: With a focus on gesture control

Neurons in Spiking Neural Networks (SNNs) communicate through spikes, similarly that neurons in the brain communicate, thus mimicking the brain. The working of SNNs is temporally based, as the spikes are time-dependent. SNNs have the benefit to perform continual classification, and are inherently more low-power than other Artificial Neural...

Neuromorphic Retina Design to encode LIDAR based Scene Dynamics

Autonomous vehicle (AV technology) relies heavily on vision based applications like object recognition, obstacle/collision avoidance etc. In order to achieve this, understanding and estimating the dynamics in the environment is extremely important. LIDARs are proven to detect both shape as well as the speed/movement of the objects in the scene...

An Object Detecting Architecture using Spiking Neural Networks

Spiking Neural Networks have opened new doors in the world of Neural Networks. This study implements and shows a viable architecture to detect and classify blob-like input data. An architecture consisting of three parts a region proposal network, weight calculations, and the classifier is discussed and implemented. The region proposal network is...

N-shot Training Methodology: For Spiking Neural Networks(SNNs)

Traditional Artificial Neural Networks(ANNs)like CNNs have shown tremendous opportunities in various domains like autonomous cars, disease diagnosis, etc. Proven learning algorithms like backpropagation help ANNs in achieving higher accuracy. But there is a serious challenge with the increasing popularity of traditional ANNs is of energy...

A Highly Concurrent, Memory-Efficient AER Architecture for Neuro-Synaptic Spike Routing

One of the challenges of neuromorphic computing is efficiently routing spikes from neurons to their connected synapses. The aim of this thesis is to design a spike-routing architecture for flexible connections on single-chip neuromorphic systems. A model for estimating area, power consumption, memory, spike latency and link utilisation for...

Design Space Exploration of a Neuromorphic ECG Classification System using a Spiking Self-Organizing Map

The Self-Organizing Map (SOM) is an unsupervised neural networktopology that incorporates competitive learning for the classicationof data. In this thesis we investigate the design space of a system incorporating such a topology based on Spiking Neural Networks (SNNs), and apply it to classifying electrocardiogram (ECG) beats. We present novel...

Digital Neuron Cells for Highly Parallel Cognitive Systems

The high level of realism of spiking neuron networks and their complexity require a considerable computational resources limiting the size of the realized networks. Consequently, the main challenge in building complex and biologically accurate spiking neuron network is largely set by the high computational and data transfer demands. In this...