AIGOR: A Modular, Event-Driven Neuromorphic Architecture for Configurable SNN Inference

Abstract

Spiking neural networks (SNNs) run today on a fragmented landscape of hardware: dedicated neuromorphic processors, application-specific FPGA accelerators, and large-scale neuroscience simulators, each typically built around a fixed neuron model, execution strategy, or workload class. We present AIGOR, a modular, event-driven neuromorphic architecture for spiking neural network inference. AIGOR organizes neurons into timestep-synchronized processing cores that exchange spikes as packets over a packet-switched communication layer, and it is assembled from a library of parameterized compute, memory, and communication IP blocks rather than as a one-off design for a single network. The neuron model, numeric precision, the folding of neurons onto hardware, and the partitioning across cores are configured per instance rather than committed at design time; a single declarative specification then generates the cores, neuron kernels, and synaptic-memory images that realize a given network. We validate a first prototype on the AMD Versal VPK180 across two deliberately different workloads mapped onto the same cores: a feedforward image classifier trained in snnTorch and a recurrent bal anced random network modeled in NEST. The classifier reproduces its snnTorch reference accuracy, and the recurrent network matches its NEST reference at spike-level precision across multiple cores spanning two FPGAs. We report post-implementation resource utilization and validate the multi-node synchronization scheme in simulation up to one thousand cores on a three-dimensional torus. The prototype's measured limits localize the throughput bottleneck in the synaptic-delivery datapath and the global timestep barrier, and motivate a set of datapath refinements, now in development, that the configurable structure of the architecture admits as changes to the same cores.