The neuromorphic world finally has a lingua franca: the Neuromorphic Intermediate Representation, a framework-agnostic graph that every major spiking-network library can export. SC-NeuroCore's distinctive move is to compile a supported NIR graph straight to synthesisable Verilog — so a model trained anywhere can be deployed to an FPGA, without rewriting the network by hand.
Pick the framework you train in. Its NIR export is the same graph SC-NeuroCore's compiler consumes, so the path to hardware is the same regardless of where the model came from.
Support is for NIR graphs the compiler covers — the primitives and topologies it maps — not every conceivable graph; unsupported nodes are reported rather than silently dropped.
NIR defines a spiking network as a directed graph of about eighteen primitives — neurons, affine maps, connections — independent of any one framework. Because the major libraries all export to it, a single importer that understands NIR understands them all. It is the neuromorphic analogue of ONNX for deep learning.
delay_steps annotation rather than being flattened away, so recurrent architectures survive the trip to hardware.The pipeline is a single flow: import the model into a graph, extract the topology, quantise the parameters to fixed-point, generate a neuron module per node, and emit synthesisable RTL — the same bit-true hardware path the rest of the toolkit uses, so the deployed circuit is provably the imported network.
Train in whatever suits the problem — snnTorch, Norse, Rockpool, Sinabs — in float32, then export NIR and deploy. For models that live in the deep-learning world, an ONNX → NIR shim brings them into the same graph, and the round-trip is checked so what you deploy matches what you trained.
Interoperability does not stop at NIR. A NeuroML importer brings in computational-neuroscience models, a SpikeInterface path ingests recorded neural data, and PyTorch/JAX bridges connect the training ecosystems — so SC-NeuroCore sits at a crossroads rather than in a silo.
NIR is what makes SC-NeuroCore a deployment target rather than another island: it does not ask you to abandon your training stack, only to export the graph everyone already agrees on — and then it does the one thing the other tools do not, which is turn that graph into silicon.