LU
L. Usa
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Novel Neuromorphic Hardware Inspired by the Olfactory Pathway Model of the Drosophila
Leveraging bio-plausible computational primitives in digital circuits for spatio-temporal processing
Olfactory learning in Drosophila larvae exemplifies efficient neural processing in a small-scale network with minimal power consumption. This system enables larvae to anticipate important outcomes based on new and familiar odor stimuli, a process crucial for survival and adaptation. Central to this learning mechanism is the olfactory pathway model, which embodies the principles of synaptic plasticity and associative learning through prediction error coding mediated by specific neuromodulating neurons in the mushroom body, like dopaminergic neurons. There is a pressing need to develop novel computational frameworks that capture the spatio-temporal processes while remaining compatible with the constraints of small-scale neural networks. These frameworks should draw inspiration from the biophysical properties of neurons within the olfactory pathway model, enabling accurate emulation of neural dynamics and efficient learning processes using spiking neural networks. This thesis proposes a framework based on a phenomenological conductance-based leaky integrate-and-fire (COBALIF) neuron model, inspired by the olfactory pathway model of Drosophila larvae. By first prototyping the spiking neural network in Intel's Lava Python-based framework, we validated the design on a neuron and system level for a neuromorphic hardware implementation. This was the foundation of a programmable, neuromorphic FPGA architecture capable of adaptive optimization, employed on a Zynq 7000 SoC FPGA. By implementing this architecture in a single-precision floating-point format, we model the real-time neural dynamics of the COBALIF neuron in one-tenth of a millisecond precision. Moreover, our FPGA implementation serves as a feasible prototype for deploying such biologically inspired neurons and their spatio-temporal dependencies in digital design, paving the way for scaling up to small-scale networks.
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Olfactory learning in Drosophila larvae exemplifies efficient neural processing in a small-scale network with minimal power consumption. This system enables larvae to anticipate important outcomes based on new and familiar odor stimuli, a process crucial for survival and adaptation. Central to this learning mechanism is the olfactory pathway model, which embodies the principles of synaptic plasticity and associative learning through prediction error coding mediated by specific neuromodulating neurons in the mushroom body, like dopaminergic neurons. There is a pressing need to develop novel computational frameworks that capture the spatio-temporal processes while remaining compatible with the constraints of small-scale neural networks. These frameworks should draw inspiration from the biophysical properties of neurons within the olfactory pathway model, enabling accurate emulation of neural dynamics and efficient learning processes using spiking neural networks. This thesis proposes a framework based on a phenomenological conductance-based leaky integrate-and-fire (COBALIF) neuron model, inspired by the olfactory pathway model of Drosophila larvae. By first prototyping the spiking neural network in Intel's Lava Python-based framework, we validated the design on a neuron and system level for a neuromorphic hardware implementation. This was the foundation of a programmable, neuromorphic FPGA architecture capable of adaptive optimization, employed on a Zynq 7000 SoC FPGA. By implementing this architecture in a single-precision floating-point format, we model the real-time neural dynamics of the COBALIF neuron in one-tenth of a millisecond precision. Moreover, our FPGA implementation serves as a feasible prototype for deploying such biologically inspired neurons and their spatio-temporal dependencies in digital design, paving the way for scaling up to small-scale networks.
Smart Teddy: Design of the Power Operations and Distribution
Elderly Monitoring and Support System Using Ambient Intelligence
With the increasing demand in home-care service to provide early intervention at the homes of seniors suffering from early stage dementia, the Smart Teddy prototype offers a technological solution to disburden caregivers, to promote and track the health and conditions of the senior and to prolong their independent life at home. The Smart Teddy is a project founded in 2018 by a research team of the Hague University of Applied Sciences. It is an interactive, companion robot - disguised as a toy dog - that serves as a therapeutic promoter while simultaneously monitoring the quality of life of the senior with pre-determined indicators. It consists of a Teddy to provide the interaction with the user and a Base Station for data processing and charging of the mount-in battery of the Teddy. The Power Operations and Distribution group carried out research on and developed suitable solutions to supply power to the Teddy with rechargeable batteries, to integrate controlled wireless charging of the battery for user-friendliness and to provide a streamlined power distribution throughout the Smart Teddy’s system. Based on an iterated program of requirements and a power budget analysis on the set of installed electronics, a design is implemented for the power system of the Smart Teddy. A design sequence is followed consisting of four stages: (1) battery selection, (2) charger selection, (3) power conversion and distribution, and (4) safety and failure protection. A power system is developed for the Smart Teddy that is able to supply power with lithium-ion batteries with a battery life of at least 12 hours providing a battery capacity of 25.16 Wh. Thereafter, the installed batteries located in the Teddy can be charged wirelessly by placing the Teddy on the Base Station (a dog bed) to reinforce the less robot-like and a more natural look of the Teddy. Furthermore, this system ensures that all electronic modules with different operating voltages and current draws are provided with the necessary power specifications through power-sharing paths and the usage of power converters. Lastly, safety measures and failure protection methods are developed to ensure the safety of the user through the usage of fuses, switches and, cable and PCB management. The design has been verified using the appropriate verification methods where the program of requirements is used as a guideline and assessment tool. The Power Operations and Distribution is largely complying with the program of requirements and is performing according to the predetermined functionalities. Consequently, the power system of the Smart Teddy is integrated in a real, spatial prototype, namely in a toy dog (the Teddy) and its dog bed (the Base Station).
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With the increasing demand in home-care service to provide early intervention at the homes of seniors suffering from early stage dementia, the Smart Teddy prototype offers a technological solution to disburden caregivers, to promote and track the health and conditions of the senior and to prolong their independent life at home. The Smart Teddy is a project founded in 2018 by a research team of the Hague University of Applied Sciences. It is an interactive, companion robot - disguised as a toy dog - that serves as a therapeutic promoter while simultaneously monitoring the quality of life of the senior with pre-determined indicators. It consists of a Teddy to provide the interaction with the user and a Base Station for data processing and charging of the mount-in battery of the Teddy. The Power Operations and Distribution group carried out research on and developed suitable solutions to supply power to the Teddy with rechargeable batteries, to integrate controlled wireless charging of the battery for user-friendliness and to provide a streamlined power distribution throughout the Smart Teddy’s system. Based on an iterated program of requirements and a power budget analysis on the set of installed electronics, a design is implemented for the power system of the Smart Teddy. A design sequence is followed consisting of four stages: (1) battery selection, (2) charger selection, (3) power conversion and distribution, and (4) safety and failure protection. A power system is developed for the Smart Teddy that is able to supply power with lithium-ion batteries with a battery life of at least 12 hours providing a battery capacity of 25.16 Wh. Thereafter, the installed batteries located in the Teddy can be charged wirelessly by placing the Teddy on the Base Station (a dog bed) to reinforce the less robot-like and a more natural look of the Teddy. Furthermore, this system ensures that all electronic modules with different operating voltages and current draws are provided with the necessary power specifications through power-sharing paths and the usage of power converters. Lastly, safety measures and failure protection methods are developed to ensure the safety of the user through the usage of fuses, switches and, cable and PCB management. The design has been verified using the appropriate verification methods where the program of requirements is used as a guideline and assessment tool. The Power Operations and Distribution is largely complying with the program of requirements and is performing according to the predetermined functionalities. Consequently, the power system of the Smart Teddy is integrated in a real, spatial prototype, namely in a toy dog (the Teddy) and its dog bed (the Base Station).