Abstract
The massively parallel nature of biological information processing plays an important role due to its superiority in comparison to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.
Highlights
The aggressive pursuit of Moore’s law in conventional computing architectures is slowly but surely nearing its end (Waldrop, 2016), with difficult-to-overcome physical effects, such as heat production and quantum uncertainty, representing the main limiting factors
In this paper we describe the realization of neural sampling with networks of leaky integrate-and-fire neurons (Petrovici et al, 2016) on the BrainScaleS accelerated neuromorphic platform (Schemmel et al, 2010)
In this work we showed how sampling-based Bayesian inference using hierarchical spiking networks can be robustly implemented on a physical model system despite inherent variability and imperfections
Summary
The aggressive pursuit of Moore’s law in conventional computing architectures is slowly but surely nearing its end (Waldrop, 2016), with difficult-to-overcome physical effects, such as heat production and quantum uncertainty, representing the main limiting factors. Some important advantages of this approach lie in their reduced power consumption and enhanced speed compared to conventional simulations of biological neuronal networks, which represent direct payoffs of replacing the resource-intensive numerical calculation of neuro-synaptic dynamics with the physics of the devices themselves. Such computation with analog dynamics, without the convenience of binarization, as used in digital devices, has a downside of its own: variability in the manufacturing process (fixed pattern noise) and temporal noise both lead to reduced controllability of the circuit dynamics.
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