PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network

Abstract

We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two key contributions. First, we provide an interface of PyNN to CARLsim, a computationally efficient, GPU-accelerated and biophysically-detailed SNN simulator. PyCARL facilitates joint development of machine learning models and code sharing between CARLsim and PyNN users, promoting an integrated and larger neuromorphic community. Second, we integrate cycle-accurate models of state-of-the-art neuromorphic hardware such as TrueNorth, Loihi, and DynapSE in PyCARL, to accurately model hardware latencies, which delay spikes between communicating neurons, degrading performance of machine learning models. PyCARL allows users to analyze and optimize the performance difference between software-based simulation and hardware-oriented simulation. We show that system designers can also use PyCARL to perform design-space exploration early in the product development stage, facilitating faster time-to-market of neuromorphic products.