T. Spyrou
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The rapid advancement of artificial intelligence (AI) and deep learning has intensified computational demands, exposing inefficiencies in traditional von Neumann architectures due to the "memory wall" problem. Computation-in-Memory (CIM) emerges as a promising paradigm, performing computations directly within memory arrays to minimize data movement and enhance energy efficiency. However, current CIM design methodologies face significant challenges in configuration generation, performance characterization, and application-specific optimization. This thesis addresses these gaps by proposing a simulation framework that enables architectural exploration of multi-tile CIM-based NN accelerators with a focus on network traffic modeling, resource utilization, and communication efficiency.
The framework abstracts NN operations into hardware-executable patterns, encompassing both fully connected and convolutional layers, and incorporates key CIM components such as crossbars, accumulators, and activation units. Specialized mapping strategies and tiling techniques are introduced to capture realistic execution, while a bandwidth-constrained interconnect model quantifies communication bottlenecks. The framework supports both major convolution-to-MVM conversion schemes, Im2Col and K2M, allowing systematic evaluation of trade-offs between latency, area, and energy efficiency. Additionally, the framework integrates a topology visualization module that automatically generates Graphviz-based diagrams of component interconnections and data flows, enabling intuitive inspection of communication patterns and design bottlenecks.
Experimental evaluation on MNIST demonstrates the framework’s capability to reveal fundamental design insights. Results show that while K2M maximizes crossbar utilization (0.8–1.0) and reduces latency by up to 100×, Im2Col achieves up to 1000× reductions in crossbar count and data transfer energy, making it favorable for area- and energy-constrained systems. Bandwidth analysis further highlights the interaction between communication capacity and optimal crossbar sizing, establishing design guidelines that balance delay, efficiency, and scalability.
Overall, this thesis contributes a flexible and extensible simulation environment for multi-tile CIM accelerators that bridges the gap between neural network workloads and CIM hardware design, providing a foundation for future architectural exploration and hybrid optimization strategies in NN accelerators. ...
The framework abstracts NN operations into hardware-executable patterns, encompassing both fully connected and convolutional layers, and incorporates key CIM components such as crossbars, accumulators, and activation units. Specialized mapping strategies and tiling techniques are introduced to capture realistic execution, while a bandwidth-constrained interconnect model quantifies communication bottlenecks. The framework supports both major convolution-to-MVM conversion schemes, Im2Col and K2M, allowing systematic evaluation of trade-offs between latency, area, and energy efficiency. Additionally, the framework integrates a topology visualization module that automatically generates Graphviz-based diagrams of component interconnections and data flows, enabling intuitive inspection of communication patterns and design bottlenecks.
Experimental evaluation on MNIST demonstrates the framework’s capability to reveal fundamental design insights. Results show that while K2M maximizes crossbar utilization (0.8–1.0) and reduces latency by up to 100×, Im2Col achieves up to 1000× reductions in crossbar count and data transfer energy, making it favorable for area- and energy-constrained systems. Bandwidth analysis further highlights the interaction between communication capacity and optimal crossbar sizing, establishing design guidelines that balance delay, efficiency, and scalability.
Overall, this thesis contributes a flexible and extensible simulation environment for multi-tile CIM accelerators that bridges the gap between neural network workloads and CIM hardware design, providing a foundation for future architectural exploration and hybrid optimization strategies in NN accelerators. ...
The rapid advancement of artificial intelligence (AI) and deep learning has intensified computational demands, exposing inefficiencies in traditional von Neumann architectures due to the "memory wall" problem. Computation-in-Memory (CIM) emerges as a promising paradigm, performing computations directly within memory arrays to minimize data movement and enhance energy efficiency. However, current CIM design methodologies face significant challenges in configuration generation, performance characterization, and application-specific optimization. This thesis addresses these gaps by proposing a simulation framework that enables architectural exploration of multi-tile CIM-based NN accelerators with a focus on network traffic modeling, resource utilization, and communication efficiency.
The framework abstracts NN operations into hardware-executable patterns, encompassing both fully connected and convolutional layers, and incorporates key CIM components such as crossbars, accumulators, and activation units. Specialized mapping strategies and tiling techniques are introduced to capture realistic execution, while a bandwidth-constrained interconnect model quantifies communication bottlenecks. The framework supports both major convolution-to-MVM conversion schemes, Im2Col and K2M, allowing systematic evaluation of trade-offs between latency, area, and energy efficiency. Additionally, the framework integrates a topology visualization module that automatically generates Graphviz-based diagrams of component interconnections and data flows, enabling intuitive inspection of communication patterns and design bottlenecks.
Experimental evaluation on MNIST demonstrates the framework’s capability to reveal fundamental design insights. Results show that while K2M maximizes crossbar utilization (0.8–1.0) and reduces latency by up to 100×, Im2Col achieves up to 1000× reductions in crossbar count and data transfer energy, making it favorable for area- and energy-constrained systems. Bandwidth analysis further highlights the interaction between communication capacity and optimal crossbar sizing, establishing design guidelines that balance delay, efficiency, and scalability.
Overall, this thesis contributes a flexible and extensible simulation environment for multi-tile CIM accelerators that bridges the gap between neural network workloads and CIM hardware design, providing a foundation for future architectural exploration and hybrid optimization strategies in NN accelerators.
The framework abstracts NN operations into hardware-executable patterns, encompassing both fully connected and convolutional layers, and incorporates key CIM components such as crossbars, accumulators, and activation units. Specialized mapping strategies and tiling techniques are introduced to capture realistic execution, while a bandwidth-constrained interconnect model quantifies communication bottlenecks. The framework supports both major convolution-to-MVM conversion schemes, Im2Col and K2M, allowing systematic evaluation of trade-offs between latency, area, and energy efficiency. Additionally, the framework integrates a topology visualization module that automatically generates Graphviz-based diagrams of component interconnections and data flows, enabling intuitive inspection of communication patterns and design bottlenecks.
Experimental evaluation on MNIST demonstrates the framework’s capability to reveal fundamental design insights. Results show that while K2M maximizes crossbar utilization (0.8–1.0) and reduces latency by up to 100×, Im2Col achieves up to 1000× reductions in crossbar count and data transfer energy, making it favorable for area- and energy-constrained systems. Bandwidth analysis further highlights the interaction between communication capacity and optimal crossbar sizing, establishing design guidelines that balance delay, efficiency, and scalability.
Overall, this thesis contributes a flexible and extensible simulation environment for multi-tile CIM accelerators that bridges the gap between neural network workloads and CIM hardware design, providing a foundation for future architectural exploration and hybrid optimization strategies in NN accelerators.
Identifying and Modulating Pathological Brain Connectivity: A Computational Approach
Patient-Specific, Virtual Brain Twins and Network Neuromodulation
Neuromodulation is a promising treatment methodology for disorders of the brain. However, current clinical applications are primarily focused on a rudimentary form of neuromodulation, exclusively targeting individual regions in the brain. This has led to limitations in effective treatment options for patients suffering from neurological and neuropsychiatric diseases, with brain stimulation yielding a success rate of only 50% in 50% of patients after five years. In recent years, it has been strongly theorized that most brain disorders are emergent properties of pathological network connectivity rather than dysfunction of isolated brain regions. Targeting brain connectivity via neuromodulation is, therefore, a promising approach for improving treatment efficacy. This study aims at bridging this therapeutic gap by advancing treatment strategies towards network-based neuromodulation. To achieve this goal, a computational framework was designed and implemented that analyzes brain activity to identify biomarkers of abnormal connectivity networks. Furthermore, software pipelines for the construction of patient-specific,
virtual brain models were designed and implemented which enable analysis of simulated neural activity and connectivity networks, and enable in silico neuromodulation. The framework is tested and validated using an EEG dataset consisting of neural-activity recordings belonging to patients diagnosed with neuropathic pain and a separate healthy control group. Our computational framework shows high ability in identifying disease-specific abnormalities in network connectivity and in constructing pathological network hubs, with accuracies up to 98.6% in explaining neuropathic pain. Patient-specific,
brain-model construction is also able to accurately represent empirical patterns of connectivity in simulation. Furthermore, the simulated neuromodulation with personalized virtual brains demonstrates the ability to accurately model dynamic modulation of brain connectivity networks. These findings illustrate the effectiveness of our overall framework and highlight the potential for integration into clinical decision-making and closed-loop implantable neuromodulation systems, enabling more effective treatment of brain disorders. ...
virtual brain models were designed and implemented which enable analysis of simulated neural activity and connectivity networks, and enable in silico neuromodulation. The framework is tested and validated using an EEG dataset consisting of neural-activity recordings belonging to patients diagnosed with neuropathic pain and a separate healthy control group. Our computational framework shows high ability in identifying disease-specific abnormalities in network connectivity and in constructing pathological network hubs, with accuracies up to 98.6% in explaining neuropathic pain. Patient-specific,
brain-model construction is also able to accurately represent empirical patterns of connectivity in simulation. Furthermore, the simulated neuromodulation with personalized virtual brains demonstrates the ability to accurately model dynamic modulation of brain connectivity networks. These findings illustrate the effectiveness of our overall framework and highlight the potential for integration into clinical decision-making and closed-loop implantable neuromodulation systems, enabling more effective treatment of brain disorders. ...
Neuromodulation is a promising treatment methodology for disorders of the brain. However, current clinical applications are primarily focused on a rudimentary form of neuromodulation, exclusively targeting individual regions in the brain. This has led to limitations in effective treatment options for patients suffering from neurological and neuropsychiatric diseases, with brain stimulation yielding a success rate of only 50% in 50% of patients after five years. In recent years, it has been strongly theorized that most brain disorders are emergent properties of pathological network connectivity rather than dysfunction of isolated brain regions. Targeting brain connectivity via neuromodulation is, therefore, a promising approach for improving treatment efficacy. This study aims at bridging this therapeutic gap by advancing treatment strategies towards network-based neuromodulation. To achieve this goal, a computational framework was designed and implemented that analyzes brain activity to identify biomarkers of abnormal connectivity networks. Furthermore, software pipelines for the construction of patient-specific,
virtual brain models were designed and implemented which enable analysis of simulated neural activity and connectivity networks, and enable in silico neuromodulation. The framework is tested and validated using an EEG dataset consisting of neural-activity recordings belonging to patients diagnosed with neuropathic pain and a separate healthy control group. Our computational framework shows high ability in identifying disease-specific abnormalities in network connectivity and in constructing pathological network hubs, with accuracies up to 98.6% in explaining neuropathic pain. Patient-specific,
brain-model construction is also able to accurately represent empirical patterns of connectivity in simulation. Furthermore, the simulated neuromodulation with personalized virtual brains demonstrates the ability to accurately model dynamic modulation of brain connectivity networks. These findings illustrate the effectiveness of our overall framework and highlight the potential for integration into clinical decision-making and closed-loop implantable neuromodulation systems, enabling more effective treatment of brain disorders.
virtual brain models were designed and implemented which enable analysis of simulated neural activity and connectivity networks, and enable in silico neuromodulation. The framework is tested and validated using an EEG dataset consisting of neural-activity recordings belonging to patients diagnosed with neuropathic pain and a separate healthy control group. Our computational framework shows high ability in identifying disease-specific abnormalities in network connectivity and in constructing pathological network hubs, with accuracies up to 98.6% in explaining neuropathic pain. Patient-specific,
brain-model construction is also able to accurately represent empirical patterns of connectivity in simulation. Furthermore, the simulated neuromodulation with personalized virtual brains demonstrates the ability to accurately model dynamic modulation of brain connectivity networks. These findings illustrate the effectiveness of our overall framework and highlight the potential for integration into clinical decision-making and closed-loop implantable neuromodulation systems, enabling more effective treatment of brain disorders.