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A SystemC SNN model for power trace generation

Kok, Wim (author)

Power analysis can be used to retrieve key information as secure systems leak data-dependent information over side channels. A proposed solution to break the correlation between side channel information and secret information was to replace a vulnerable part of the cryptography implementation with a neural network. This uses the inherent...

master thesis 2024

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

Hettema, Bart (author)

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,...

master thesis 2024

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Digital self-timed neuron design for Spiking Neuron Networks

Du, Tianyu (author)

Spiking Neural Networks(SNN) have been widely leveraged by neuromorphic systems due to their ability to closely mimic biological neural behavior, where information is exchanged and received between neurons in the form of sparse events(spikes). Such neuromorphic systems are highly energy-efficient because the use of a global clock can be avoided...

master thesis 2023

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On-chip Self Timed SNN Custom Digital Interconnect System

Huang, Jiongyu (author)

A Spiking neural network (SNN) is a type of artificial neural network which encodes information using spike timing, network structure, and synaptic weights to emulate the information processing function of the human brain. Within an SNN, it is always required to support the spike transmission that travels between neurons(array). This thesis aims...

master thesis 2023

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Self-timed interconnect for SNN - From Point to Point Communication to Multi-array Segmented-bus Solution

Zhang, Jinyao (author)

Spiking Neural Networks use Address Event Representation to communicate among different Neuron Arrays. To mimic the behavior of the human neural system and meets the requirement for large Neuron Array communication, the AER interconnect should be area-saving, have low power, and operates at high speed.<br/>This thesis aims to build self-timed...

master thesis 2022

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A New Logarithmic Quantization Technique and Corresponding Processing Element Design for CNN Accelerators

Jiang, Longxing (author)

Convolutional Neural Networks (CNN) have become a popular solution for computer vision problems. However, due to the high data volumes and intensive computation involved in CNNs, deploying CNNs on low-power hardware systems is still challenging.<br/>The power consumption of CNNs can be prohibitive in the most common implementation platforms:...

master thesis 2022

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Mapping of Spiking Neural Network Architecture using VPR

Long, Jinyun (author)

As the new generation of neural networks, Spiking Neural Network architectures<br/>executes on specialized Neuromorphic devices. The mapping of Spiking Neural Network architectures affects the power consumption and performance of the system. The target platform of the thesis is a hardware platform with Neuromorphic Arrays with columns for neural...

master thesis 2022

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Off-chip Self-timed SNN Custom Digital Interconnect System

Yang, Yichen (author)

To support the spike propagates between neurons, neuromorphic computing systems always require a high-speed communication link. <br/>Meanwhile, spiking neural networks are event-driven so that the communication links normally exclude the clock signal and related blocks. This thesis aims to develop a self-timed off-chip interconnect system with...

master thesis 2022

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Modeling of router structure for SNN-applicable NoC definitions

Zhou, Yongkang (author)

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...

master thesis 2022

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Application of Emerging Memory Technologies for Spiking Neural Networks

Buis, Jan Maarten (author)

Renewed interest in memory technologies such as memristors and ferroelectric devices can provide opportunities for traditional and non-traditional computing systems alike. To make versatile, reprogrammable AI hardware possible, neuromorphic systems are in need of a low-power, non-volatile and analog memory solution to store the weights of the...

master thesis 2022

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Spiking CA-CFAR Implementation for Radar Target Detection

van Otterloo, Bas (author)

Radar systems have been used for decades to detect targets on the ground and in the air. The radar signal is transformed into a range-doppler image that distinguishes each detected object by range and velocity for further processing. A target detection algorithm is used to filter noise and clutter. Each target can be in a region with a different...

master thesis 2021

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Hardware Spiking Neural Network based Sbox AES

Ma, Hanyu (author)

Hardware cryptographic algorithm implementation is easy to attack by side-channel attacks. The power-based side-channel attacks are powerful among several side-channel attacks. This attack methods use the relationship between the leakage model and power traces to reveal the secret key. Some existing countermeasures like mask and hide can protect...

master thesis 2021

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Temporal Synchronization of Sensors

Manjunath, Tanmay (author)

Advanced automotive vehicles are based on the real-time fusion of an increasing number of automotive sensors. For precise fusion of different sensors, measurements need to be synchronized both temporally and spatially. This thesis aims to design a hardware temporal synchronization block as part of the PRISTINE systolic array accelerator project...

master thesis 2021

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Hybrid Posit and Fixed Point Hardware for Quantized DNN Inference

Kleijweg, Zep (author)

The recently introduced posit number system was designed as a replacement for IEEE 754 floating point, to alleviate some of its shortcomings. As the number distribution of posits is similar to the data distributions in deep neural networks (DNNs), posits offer a good alternative to fixed point numbers in DNNs: using posits can result in high...

master thesis 2021

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Designing Asynchronous Gate Library with new System Level Trade-offs

YANG, FANG (author)

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...

master thesis 2021

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Efficient implementation of an audio preprocessing algorithm for SNN keyword spotting

Hijlkema, Sybold (author)

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...

master thesis 2021

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Temporal Delta Layer: Training Towards Brain Inspired Temporal Sparsity for Energy Efficient Deep Neural Networks

Preetha Vijayan, Preetha (author)

In the recent past, real-time video processing using state-of-the-art deep neural networks (DNN) has achieved human-like accuracy but at the cost of high energy consumption, making them infeasible for edge device deployment. The energy consumed by running DNNs on hardware accelerators is dominated by the number of memory read/writes and...

master thesis 2021

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Mapping of Spiking Neural Network Topologies on Neuromorphic Hardware

Kshirasagar, Shreya Sanjeev (author)

As we move towards edge computing, not only low power but concurrently, critical timing is demanded from the underlying hardware platform. Spiking neural networks ensure high performance and low power when run on specialized architectures like neuromorphic hardware. However, the techniques in use to configure these neural networks on massively...

master thesis 2021

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A Spiking Neural Network classification architecture for spatial-temporal data processing

Arriëns, Roy (author)

A big catalyst of the AI revolution has been Artificial Neural Networks (ANN), abstract computation models based on the biological neural networks in the brain. However, they require an immense amount of computational resources and power to configure and when deployed often are dependent on cloud resources to function. This makes ANNs less...

master thesis 2021

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

Li, Pai (author)

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...

master thesis 2021