Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.

ABSTRACT: Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational

SUBMITTER: Pedersen JE 

PROVIDER: S-EPMC11405706 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature