Interesting research paper, the university is linked to a few groups so I am not sure who the research was for yet. (if anyone)
StereoSpike: Depth Learning with a Spiking Neural Network
https://arxiv.org/pdf/2109.13751.pdf
Federal University of Toulouse - France
The last line of the conclusion mentions Brainchip Akida.
Abstract Breif
"Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics."
View attachment 1080
Conclusion Breif
4.4.1 Target Hardware Our model has resolutely been developed in the philosophy of spiking neural networks. As a result, it is essentially implementable on dedicated neuromorphic hardware,such as
Intel Loihi [3], IBM TrueNorth [1]. These chips can leverage the binarity and sparsity of spike tensors navigating through the network. In addition, we believe that our model being feedforward and requiring a reset on all of its neurons at each timestep is not a problem, because resetting membrane potentials is actually less costly than applying a leak. Therefore, statelesness can be seen as an advantage over recurrence in spiking models with similar performances. However, we are aware that current neuromorphic chips are initially designed for the implementation of stateful units, and acknowledge that we do not leverage this feature. Consequently, we believe that it rather fits to dedicated hardware for stateless models with sparse activations quantized on 1 bit. We therefore consider that
Brainchip’s Akida chip [35] is a good fit.