arXiv subjects

Christian Georg Mayr

Publications and source records attributed to Christian Georg Mayr.

3 recordsLinked to original sources

arXiv2025

A UDP Packet Format Establishing Adress Event Representation Communication Between Remote Neuromorphic and Biological Setups

In the field of brain-machine interfaces, biohybrids offer an interesting new perspective, as in them, the technological side acts like a closed-loop extension or real counterpart of biological tissue, instead of the usual open loop approaches in tranditional BMI. To achieve a credible counterpart to biological tissue, biohybrids usually employ one or several neuromorphic components as the hardware half of the biohybrid. However, advanced neuromorphic circuit such as memristor crossbars usually operate best in a dedicated lab with corresponding support equipment. The same is true for biological tissue, which makes co-locating all of the parts of a biohybrid in the same lab challenging. Here, we present as solution to this co-location issue a simple method to connect biohybrids via the internet by a custom UDP packet format. We show that the characteristics achieved with our solution (jitter, delay, packet loss, packet reordering) on a standard internet connection are compatible with various biohybrid processing paradigms, and we present a short three-ways experiment as proof-of-concept. The described UDP format has been employed to link biohybrids and neuromorphic circuits in four different EC-funded projects.

q-bio.NC↗

arXiv2024

Fast Switching Serial and Parallel Paradigms of SNN Inference on Multi-core Heterogeneous Neuromorphic Platform SpiNNaker2

With serial and parallel processors introduced into Spiking Neural Networks (SNNs) execution, more and more researchers are dedicated to improving the performance of the computing paradigms by taking full advantage of the strengths of the available processor. In this paper, we compare and integrate serial and parallel paradigms into one SNN compiling system. For a faster switching between them in the layer granularity, we train the classifier to prejudge a better paradigm before compiling instead of making the decision afterward, saving a great amount of compiling time and RAM space on the host PC. The classifier Adaptive Boost, with the highest accuracy (91.69%) among 12 classifiers, is integrated into the switching system, which utilizes less memory and processors on the multi-core neuromorphic hardware backend SpiNNaker2 than two individual paradigms. To the best of our knowledge, it is the first fast-switching compiling system for SNN simulation.

cs.DC↗

arXiv2022

Convolutional Neural Networks Quantization with Attention

It has been proven that, compared to using 32-bit floating-point numbers in the training phase, Deep Convolutional Neural Networks (DCNNs) can operate with low precision during inference, thereby saving memory space and power consumption. However, quantizing networks is always accompanied by an accuracy decrease. Here, we propose a method, double-stage Squeeze-and-Threshold (double-stage ST). It uses the attention mechanism to quantize networks and achieve state-of-art results. Using our method, the 3-bit model can achieve accuracy that exceeds the accuracy of the full-precision baseline model. The proposed double-stage ST activation quantization is easy to apply: inserting it before the convolution.

cs.AI↗