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With the remarkable progress that technology has made, the need forprocessing data near the sensors at the edge has increased dramatically. Theelectronic systems used in these applications must process data continuously,in real-time, and extract relevant information using the smallest possibleenergy budgets. A promising approach for implementing always-on processing ofsensory signals that supports on-demand, sparse, and edge-computing is to takeinspiration from biological nervous system. Following this approach, we presenta brain-inspired platform for prototyping real-time event-based Spiking NeuralNetworks (SNNs). The system proposed supports the direct emulation of dynamicand realistic neural processing phenomena such as short-term plasticity, NMDAgating, AMPA diffusion, homeostasis, spike frequency adaptation,conductance-based dendritic compartments and spike transmission delays. Theanalog circuits that implement such primitives are paired with a low latencyasynchronous digital circuits for routing and mapping events. This asynchronousinfrastructure enables the definition of different network architectures, andprovides direct event-based interfaces to convert and encode data fromevent-based and continuous-signal sensors. Here we describe the overall systemarchitecture, we characterize the mixed signal analog-digital circuits thatemulate neural dynamics, demonstrate their features with experimentalmeasurements, and present a low- and high-level software ecosystem that can beused for configuring the system. The flexibility to emulate differentbiologically plausible neural networks, and the chip's ability to monitor bothpopulation and single neuron signals in real-time, allow to develop andvalidate complex models of neural processing for both basic research andedge-computing applications.

DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor