Caspian: A Neuromorphic Development Platform

Current neuromorphic systems often may be difficult to use and costly to deploy. There exists a need for a simple yet flexible neuromorphic development platform which can allow researchers to quickly prototype ideas and applications. Caspian offers a high-level API along with a fast spiking simulator to enable the rapid development of neuromorphic solutions. It further offers an FPGA architecture that allows for simplified deployment -- particularly in SWaP (size, weight, and power) constrained environments. Leveraging both software and hardware, Caspian aims to accelerate development and deployment while enabling new researchers to quickly become productive with a spiking neural network system.

[1]  Garrick Orchard,et al.  SLAYER: Spike Layer Error Reassignment in Time , 2018, NeurIPS.

[2]  Romain Brette,et al.  The Brian Simulator , 2009, Front. Neurosci..

[3]  Catherine D. Schuman,et al.  An evolutionary optimization framework for neural networks and neuromorphic architectures , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[4]  Nachiket Kapre,et al.  Implementing NEF Neural Networks on Embedded FPGAs , 2018, 2018 International Conference on Field-Programmable Technology (FPT).

[5]  Hong Wang,et al.  Loihi: A Neuromorphic Manycore Processor with On-Chip Learning , 2018, IEEE Micro.

[6]  Karlheinz Meier,et al.  A mixed-signal universal neuromorphic computing system , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

[7]  Kathleen E. Hamilton,et al.  Non-Neural Network Applications for Spiking Neuromorphic Hardware , 2013 .

[8]  Marc-Oliver Gewaltig,et al.  NEST (NEural Simulation Tool) , 2007, Scholarpedia.

[9]  Bernard Brezzo,et al.  TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip , 2015, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[10]  Catherine D. Schuman,et al.  A Survey of Neuromorphic Computing and Neural Networks in Hardware , 2017, ArXiv.

[11]  Kathleen E. Hamilton,et al.  Spike-based primitives for graph algorithms , 2019, ArXiv.

[12]  Darpan T. Sanghavi,et al.  BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python , 2018, Front. Neuroinform..

[13]  Catherine D. Schuman,et al.  DANNA: A neuromorphic software ecosystem ☆ , 2016, BICA 2016.

[14]  Trevor Bekolay,et al.  Nengo: a Python tool for building large-scale functional brain models , 2014, Front. Neuroinform..

[15]  Mark E. Dean,et al.  DANNA 2: Dynamic Adaptive Neural Network Arrays , 2018, Proceedings of the International Conference on Neuromorphic Systems.

[16]  Gregory Cohen,et al.  Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades , 2015, Front. Neurosci..

[17]  Steve B. Furber,et al.  The SpiNNaker Project , 2014, Proceedings of the IEEE.

[18]  Catherine D. Schuman,et al.  Non-Traditional Input Encoding Schemes for Spiking Neuromorphic Systems , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[19]  Wayne Luk,et al.  NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors , 2016, Front. Neurosci..

[20]  Kathleen E. Hamilton,et al.  Shortest Path and Neighborhood Subgraph Extraction on a Spiking Memristive Neuromorphic Implementation , 2019, NICE '19.

[21]  Pierre Yger,et al.  PyNN: A Common Interface for Neuronal Network Simulators , 2008, Front. Neuroinform..