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PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
Conference proceeding   Open access

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

A Balaji, P Adiraju, H J Kashyap, A Das, J L Krichmar, N D Dutt and F Catthoor
Proceedings of International Joint Conference on Neural Networks / co-sponsored by Japanese Neural Network Society (JNNS) [and others]
28 Sep 2020
DOI: https://doi.org/10.1109/IJCNN48605.2020.9207142
url
https://arxiv.org/pdf/2003.09696View
SubmittedarXiv.org - Non-exclusive license to distribute,  Open

Abstract

CARLsim co-simulation cs.NE design-space exploration neuromorphic computing spiking neural network
We present PyCARL, a PyNN-based common Python programming interface for hardware-software cosimulation 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.

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