nAIxt: A Light-Weight Processor Architecture for Efficient Computation of Neuron Models

The simulation of biological neural networks holds immense promise for advancing both neuroscience and artificial intelligence. Due to its high complexity, it requires powerful computers. However, the high proportion of communication and routing makes general-purpose processing architectures, as used in supercomputers, inefficient. Dedicated hardware, such as ASICs, on the other hand, can be specifically adapted to this type of workload. However, integrated circuits are rigid, thereby eliminating the use of future neuron models. To address this contradiction, this paper presents a programmable architecture for the computation of neuron models. Thanks to its Turing completeness, it enables embedding biological neural networks simulators into integrated circuits while simultaneously allowing adaptation of the neuron model. To assess suitability, both dedicated circuits and off-the-shelf processors are examined regarding AT efficiency. The proposed versatile architecture turns out to be up to 1800x more area efficient than a RISC-V processor, thereby playing a vital role in accelerating neuroscience simulation and research in AI.