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

TL;DR

DYNAP-SE2 is a scalable neuromorphic processor that emulates complex neural dynamics in real-time, enabling energy-efficient edge computing and advanced neural modeling.

Contribution

This paper introduces DYNAP-SE2, a novel neuromorphic platform supporting detailed biological neural phenomena with integrated analog-digital circuits and flexible architecture.

Findings

Supports real-time emulation of neural plasticity and dynamics

Demonstrates low-latency event-based processing

Enables complex neural network validation

Abstract

With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuously, in real-time, and extract relevant information using the smallest possible energy budgets. A promising approach for implementing always-on processing of sensory signals that supports on-demand, sparse, and edge-computing is to take inspiration from biological nervous system. Following this approach, we present a brain-inspired platform for prototyping real-time event-based Spiking Neural Networks (SNNs). The system proposed supports the direct emulation of dynamic and realistic neural processing phenomena such as short-term plasticity, NMDA gating, AMPA diffusion, homeostasis, spike frequency adaptation, conductance-based dendritic compartments and spike transmission delays. The analog circuits that implement such primitives are paired with a low latency asynchronous digital circuits for routing and mapping events. This asynchronous infrastructure enables the definition of different network architectures, and provides direct event-based interfaces to convert and encode data from event-based and continuous-signal sensors. Here we describe the overall system architecture, we characterize the mixed signal analog-digital circuits that emulate neural dynamics, demonstrate their features with experimental measurements, and present a low- and high-level software ecosystem that can be used for configuring the system. The flexibility to emulate different biologically plausible neural networks, and the chip's ability to monitor both population and single neuron signals in real-time, allow to develop and validate complex models of neural processing for both basic research and edge-computing applications.