7 research outputs found
The Yin-Yang dataset
The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks. It serves as an alternative to classic deep learning datasets, especially in early-stage prototyping scenarios for both network models and hardware platforms, for which it provides several advantages. First, it is smaller and therefore faster to learn, thereby being better suited for small-scale exploratory studies in both software simulations and hardware prototypes. Second, it exhibits a very clear gap between the accuracies achievable using shallow as compared to deep neural networks. Third, it is easily transferable between spatial and temporal input domains, making it interesting for different types of classification scenarios
Gradient-based methods for spiking physical systems
Recent efforts have fostered significant progress towards deep learning in
spiking networks, both theoretical and in silico. Here, we discuss several
different approaches, including a tentative comparison of the results on
BrainScaleS-2, and hint towards future such comparative studies.Comment: 2 page abstract, submitted to and accepted by the NNPC (International
conference on neuromorphic, natural and physical computing
Fast and deep: energy-efficient neuromorphic learning with first-spike times
For a biological agent operating under environmental pressure, energy
consumption and reaction times are of critical importance. Similarly,
engineered systems also strive for short time-to-solution and low
energy-to-solution characteristics. At the level of neuronal implementation,
this implies achieving the desired results with as few and as early spikes as
possible. In the time-to-first-spike-coding framework, both of these goals are
inherently emerging features of learning. Here, we describe a rigorous
derivation of learning such first-spike times in networks of leaky
integrate-and-fire neurons, relying solely on input and output spike times, and
show how it can implement error backpropagation in hierarchical spiking
networks. Furthermore, we emulate our framework on the BrainScaleS-2
neuromorphic system and demonstrate its capability of harnessing the chip's
speed and energy characteristics. Finally, we examine how our approach
generalizes to other neuromorphic platforms by studying how its performance is
affected by typical distortive effects induced by neuromorphic substrates.Comment: 20 pages, 8 figure
The BrainScaleS-2 Neuromorphic Platform — A Report on the Integration and Operation of an Open Science Hardware Platform within EBRAINS
This report presents the challenges encountered and the solutions created for the operation of the BrainScaleS neuromorphic platform, and the overall progress leading to this state at the end of the Human Brain Project (HBP)
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability, and efficiency
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
We present first experimental results on the novel BrainScaleS-2 neuromorphic
architecture based on an analog neuro-synaptic core and augmented by embedded
microprocessors for complex plasticity and experiment control. The high
acceleration factor of 1000 compared to biological dynamics enables the
execution of computationally expensive tasks, by allowing the fast emulation of
long-duration experiments or rapid iteration over many consecutive trials. The
flexibility of our architecture is demonstrated in a suite of five distinct
experiments, which emphasize different aspects of the BrainScaleS-2 system