Sort of yes; Xylo is a general-purpose SNN accelerator, but the scale is for smaller problems, in the order of 1000 neurons.

But in principle there's nothing standing in the way of building a 100B parameter SNN.

Yes, a few options. Rockpool is designed to work with SNN chips from SynSense (https://synsense.ai ). Intel has Loihi, there is also Akida from BrainChip…

This manuscript describes the details for neuron and synapse simulations for a mixed-signal neuromorphic spiking NN device, as well as the training, quantisation and deployment pipeline.

The idea is to build SNN applications, trained using gradient methods, that are robust against the mismatch exhibited by mixed-signal devices in general. By including a detailed trainable simulation of the neuron and synapse models, as well as trainable hardware-verified parameter mismatch models, you can perform backprop training of SNNs that are still functional when deployed to hardware, without per-chip calibration or tweaking.

Previously it was very difficult to build functioning SNNs for these devices, requiring lots of hand-tweaking and/or device calibration. With these new tools the aim is to train once, then deploy at scale to many chips, with some guarantees about performance degradation.

We've integrated these tools into the open-source deep SNN library Rockpool (https://rockpool.ai).

Manuscript abstract:

Mixed-signal neuromorphic processors provide extremely low-power operation for edge inference workloads, taking advantage of sparse asynchronous computation within Spiking Neural Networks (SNNs). However, deploying robust applications to these devices is complicated by limited controllability over analog hardware parameters, unintended parameter and dynamics variations of analog circuits due to fabrication non-idealities. Here we demonstrate a novel methodology for offline training and deployment of spiking neural networks (SNNs) to the mixed-signal neuromorphic processor Dynap-SE2. The methodology utilizes an unsupervised weight quantization method to optimize the network's parameters, coupled with adversarial parameter noise injection during training. The optimized network is shown to be robust to the effects of quantization and device mismatch, making the method a promising candidate for real-world applications with hardware constraints. This work extends Rockpool, an open-source deep-learning library for SNNs, with support accurate simulation of mixed-signal SNN dynamics. Our approach simplifies the development and deployment process for the neuromorphic community, making mixed-signal neuromorphic processors more accessible to researchers and developers.