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Here is another preprint published yesterday, in which Prof. Osvaldo Simeone from King’s College London is listed as one of the authors. His five co-authors are all from Berlin (Fraunhofer Heinrich Hertz Institute and Technical University of Berlin).
While no specific neuromorphic hardware is mentioned (although the paper’s first reference is to a 2021 paper by Intel researchers on Loihi and the penultimate one is to Prophesee’s EVK4 HD), it is nevertheless an interesting article as it shows that the wireless community is at the forefront of advancing cutting-edge technologies and thus confirms what ISL’s CEO Dr. Joe Guerci said in the recent From The Crow’s Nest podcast:
fromthecrowsnest.transistor.fm
“Well, and to amplify that point, all the advanced capabilities that we have in our RF systems, radar and EW, most of that is driven by the wireless community, the trillion-dollar wireless community compared to a paltry radar and EW ecosystem.”
Also note the funding supported by the governments of Germany and the UK as well as through the EU’s Horizon Europe funding programme (luckily for King’s College London, post-Brexit UK it is still an associated country…)
VII. CONCLUSIONS
In this work, we introduced a new system solution for device-edge co-inference that targets energy efficiency at the end device using neuromorphic hardware and signal processing, while implementing conventional radio and computing technologies at the edge. The investigated communication scheme combines on-device SNN and server-based ANN, leveraging variational directed information bottleneck technique to perform inference tasks. From a deployment perspective, our model demonstrates superior performance, less need for communication overhead and robustness under time-varying channel conditions, implying promising potential in the further 6G work. Aspects of the proposed system solution were validated in a preliminary testbed setup that implements a wireless robotic control application based on gesture recognition via a neuromorphic sensor. The testbed setup is currently being expanded to integrate end-to-end learning via an impulse-radio communication link. The proposed architecture and the corresponding testbed setup are general in the sense that they can support the implementation of different semantic tasks. Besides applications in robotics, in the future, we will also consider bio-medical applications that leverage the energy and communication efficiency of this architecture.
While no specific neuromorphic hardware is mentioned (although the paper’s first reference is to a 2021 paper by Intel researchers on Loihi and the penultimate one is to Prophesee’s EVK4 HD), it is nevertheless an interesting article as it shows that the wireless community is at the forefront of advancing cutting-edge technologies and thus confirms what ISL’s CEO Dr. Joe Guerci said in the recent From The Crow’s Nest podcast:
From the Crows' Nest | Transcript: Advancing Cognitive Warfare: Unveiling Neuromorphic Frontiers
In this episode of From the Crow’s Nest, host Ken Miller talks to author Dr. Joseph Guerci about the evolution and outlook for cognitive electronic warfare systems. Dr. Guerci is an internationally...
“Well, and to amplify that point, all the advanced capabilities that we have in our RF systems, radar and EW, most of that is driven by the wireless community, the trillion-dollar wireless community compared to a paltry radar and EW ecosystem.”
Also note the funding supported by the governments of Germany and the UK as well as through the EU’s Horizon Europe funding programme (luckily for King’s College London, post-Brexit UK it is still an associated country…)
VII. CONCLUSIONS
In this work, we introduced a new system solution for device-edge co-inference that targets energy efficiency at the end device using neuromorphic hardware and signal processing, while implementing conventional radio and computing technologies at the edge. The investigated communication scheme combines on-device SNN and server-based ANN, leveraging variational directed information bottleneck technique to perform inference tasks. From a deployment perspective, our model demonstrates superior performance, less need for communication overhead and robustness under time-varying channel conditions, implying promising potential in the further 6G work. Aspects of the proposed system solution were validated in a preliminary testbed setup that implements a wireless robotic control application based on gesture recognition via a neuromorphic sensor. The testbed setup is currently being expanded to integrate end-to-end learning via an impulse-radio communication link. The proposed architecture and the corresponding testbed setup are general in the sense that they can support the implementation of different semantic tasks. Besides applications in robotics, in the future, we will also consider bio-medical applications that leverage the energy and communication efficiency of this architecture.
