Electronics Lab –
Aristotle University of Thessaloniki (AUTh) – Department of Electrical and Computer Engineering
AUTh brings extensive expertise in digital and analog/RF circuit design, along with integrated systems technologies. The research team specializes in interconnect analysis, including pioneering work on skyrmionic interconnects, and design methodologies for emerging technologies such as 2.5D and 3D integration. AUTh’s contributions to the project include developing circuit-level models for novel computing devices and interconnects that support diverse magnetic spin-textures. Additionally, AUTh will evaluate various neural network architectures incorporating these new components and will design the electronics for a demonstrator showcasing the capabilities of the technology. This demonstration aims to highlight the unprecedented energy efficiency of the technology, potentially integrating diverse spin-textures to emulate distinct synaptic connections within neural network layers.
Competence relevant to
SkyANN
Our Electronics Lab at AUTh excels in advanced hardware design and interdisciplinary research. We have successfully undertaken pioneering projects such as AcubeSAT, a high-precision biological experiment in space, and a portable optical blood scattering sensor, showcasing our capabilities in miniaturization, sensor integration, and system optimization. Specializing in digital and analog circuit design, testing and fault diagnosis, analog signal processing, and neural network implementations, we also excel in integrated inductor design and CAD tool development. Our lab is equipped with state-of-the-art infrastructure and experimental setups, enabling international collaborations and innovative research projects.
In the SkyANN project, our lab will leverage its expertise to develop physical and compact models of skyrmionic components. These models will capture the operational aspects of skyrmionic building blocks, facilitating accurate simulations and benchmarking for large-scale neural networks. Our proficiency in creating analytical models and translating complex simulations into equivalent circuit models will optimize neural functionality and ensure robust performance. We will contribute to the experimental evaluation and development of CMOS interfaces for the skyrmionic neural network. Our experience in sensor integration and analog signal processing will be vital in designing and fabricating interfaces that connect neural network layers, enabling effective edge inference and learning. Additionally, our lab will perform extensive simulations to benchmark the performance of skyrmionic neural networks against state-of-the-art CMOS and possibly other emerging technologies. By demonstrating the energy efficiency, speed, and accuracy of these networks, we will help establish the suitability of skyrmionic devices for diverse neural network architectures and edge computing applications.
Through our contributions, our Electronics Lab will play an important role in advancing the SkyANN project, leveraging our extensive experience and technical expertise to develop innovative, energy-efficient neural network hardware.
Role in the
project
AUTh’s Electronics Lab contributes to the SkyANN project by developing physical and compact models of skyrmionic components, also acting as a Lead partner of T3.5, which handles the design of CMOS interfaces for experimental neural network evaluation. We also lead T4.4 relating to the circuit-level performance benchmarking in order to demonstrate energy efficiency and accuracy against state-of-the-art technologies.
Contact persons
Vasilis F. Pavlidis, vpavlid@ece.auth.gr
Electronics Lab –
Aristotle University of Thessaloniki (AUTh) – Department of Electrical and Computer Engineering
AUTh brings extensive expertise in digital and analog/RF circuit design, along with integrated systems technologies. The research team specializes in interconnect analysis, including pioneering work on skyrmionic interconnects, and design methodologies for emerging technologies such as 2.5D and 3D integration. AUTh’s contributions to the project include developing circuit-level models for novel computing devices and interconnects that support diverse magnetic spin-textures. Additionally, AUTh will evaluate various neural network architectures incorporating these new components and will design the electronics for a demonstrator showcasing the capabilities of the technology. This demonstration aims to highlight the unprecedented energy efficiency of the technology, potentially integrating diverse spin-textures to emulate distinct synaptic connections within neural network layers.
Competence relevant to
SkyANN
Our Electronics Lab at AUTh excels in advanced hardware design and interdisciplinary research. We have successfully undertaken pioneering projects such as AcubeSAT, a high-precision biological experiment in space, and a portable optical blood scattering sensor, showcasing our capabilities in miniaturization, sensor integration, and system optimization. Specializing in digital and analog circuit design, testing and fault diagnosis, analog signal processing, and neural network implementations, we also excel in integrated inductor design and CAD tool development. Our lab is equipped with state-of-the-art infrastructure and experimental setups, enabling international collaborations and innovative research projects.
In the SkyANN project, our lab will leverage its expertise to develop physical and compact models of skyrmionic components. These models will capture the operational aspects of skyrmionic building blocks, facilitating accurate simulations and benchmarking for large-scale neural networks. Our proficiency in creating analytical models and translating complex simulations into equivalent circuit models will optimize neural functionality and ensure robust performance. We will contribute to the experimental evaluation and development of CMOS interfaces for the skyrmionic neural network. Our experience in sensor integration and analog signal processing will be vital in designing and fabricating interfaces that connect neural network layers, enabling effective edge inference and learning. Additionally, our lab will perform extensive simulations to benchmark the performance of skyrmionic neural networks against state-of-the-art CMOS and possibly other emerging technologies. By demonstrating the energy efficiency, speed, and accuracy of these networks, we will help establish the suitability of skyrmionic devices for diverse neural network architectures and edge computing applications.
Through our contributions, our Electronics Lab will play an important role in advancing the SkyANN project, leveraging our extensive experience and technical expertise to develop innovative, energy-efficient neural network hardware.
Role in the
project
AUTh’s Electronics Lab contributes to the SkyANN project by developing physical and compact models of skyrmionic components, also acting as a Lead partner of T3.5, which handles the design of CMOS interfaces for experimental neural network evaluation. We also lead T4.4 relating to the circuit-level performance benchmarking in order to demonstrate energy efficiency and accuracy against state-of-the-art technologies.
Contact persons
Vasilis F. Pavlidis, vpavlid@ece.auth.gr
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