SuperNeuronBench - A Computational Benchmark for Comparing Time Multiplexed Neuromorphic Architectures on Open Source FPGAs to Parallel Biological Software Models

The real-time simulation of biological neurons is a constraint in computational neuroscience. One human brain cortical column includes tens of thousands of neurons and hundreds of millions to billions of synaptic connections that have to update in the same clock cycle. Time multiplexing is the scientific method used to mitigate this problem. One processing element, named a "superneuron", cycles through the states of simulated neurons in a sequence. This method is used by systems such as SpiNNaker, Intel Loihi, and IBM TrueNorth. But this sequential process creates a time gap that does not exist in the parallel process of biological neurons. No study isolates the effect of this timing gap on neural computation types, and this problem is not addressed for open-source neuromorphic hardware such as NeuroCoreX. This proposal introduces SuperNeuronBench, a benchmark that measures the reduction in biological accuracy caused by time multiplexing across task types. Those task types are pattern storage and retrieval, spike-timing-dependent plasticity(STDP), temporal gating, and coincidence detection. The benchmark uses the van Rossum spike-train distance against a parallel NEST or Brian2 software-biological reference to process vand calculate a normalized biological accuracy value, across superneuron configurations. Those configurations are x, the number of neurons per superneuron; z, the number of time-block subdivisions, and C, the number of superneurons in parallel. Testing will be done using open-source models on Intel Altera Cyclone IV EP4CE15 and Cyclone V SoC hardware. The main hypothesis are that time-dependent tasks reduce the accuracy at a lower multiplexing density than rate-coded tasks.