Abstract
Future developments in artificial intelligence will profit from the existence of novel, non-traditional substrates for brain-inspired computing. Neuromorphic computers aim to provide such a substrate that reproduces the brain’s capabilities in terms of adaptive, low-power information processing. We present results from a prototype chip of the BrainScaleS-2 mixed-signal neuromorphic system that adopts a physical-model approach with a 1000-fold acceleration of spiking neural network dynamics relative to biological real time. Using the embedded plasticity processor, we both simulate the Pong arcade video game and implement a local plasticity rule that enables reinforcement learning, allowing the on-chip neural network to learn to play the game. The experiment demonstrates key aspects of the employed approach, such as accelerated and flexible learning, high energy efficiency and resilience to noise.
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References
Hassabis, D., et al.: Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017). https://doi.org/10.1016/j.neuron.2017.06.011
Dean, J., et al.: A new golden age in computer architecture: empowering the machine-learning revolution. IEEE Micro 38(2), 21–29 (2018). https://doi.org/10.1109/MM.2018.112130030. ISSN 0272-1732
Schuman, C.D., et al.: A survey of neuromorphic computing and neural networks in hardware. CoRR abs/1705.06963 (2017)
Friedmann, S., et al.: Demonstrating hybrid learning in a flexible neuromorphic hardware system. IEEE Trans. Biomed. Circ. Syst. 11(1), 128–142 (2017). https://doi.org/10.1109/TBCAS.2016.2579164
Wunderlich, T., et al.: Demonstrating advantages of neuromorphic computation: a pilot study. Front. Neurosci. 13, 260 (2019). https://doi.org/10.3389/fnins.2019.00260
Fréemaux, N., et al.: Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules. Front. Neural Circ. 9, 85 (2015). https://doi.org/10.3389/fncir.2015.00085. ISSN 1662-5110
Jordan, J., et al.: Extremely scalable spiking neuronal network simulation code: from laptops to exascale computers. Front. Neu roinform. 12, 2 (2018). https://doi.org/10.3389/fninf.2018.00002. ISSN 1662-5196
van Albada, S.J., et al.: Performance comparison of the digital neuromorphic hardware SpiNNaker and the neural network simulation software NEST for a full-scale cortical microcircuit model. Front. Neurosci. 12, 291 (2018). https://doi.org/10.3389/fnins.2018.00291. ISSN 1662-453X
Mikaitis, M., et al.: Neuromodulated synaptic plasticity on the SpiNNaker neuromorphic system. Front. Neurosci. 12, 105 (2018). https://doi.org/10.3389/fnins.2018.00105. ISSN 1662-453X
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Wunderlich, T., Kungl, A.F., Müller, E., Schemmel, J., Petrovici, M. (2019). Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science(), vol 11727. Springer, Cham. https://doi.org/10.1007/978-3-030-30487-4_10
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DOI: https://doi.org/10.1007/978-3-030-30487-4_10
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