The energy used to perform this operation is so low that a wearable medical device could work for years on battery power using the BrainScaleS ASIC. The BrainScaleS architecture can be accessed and tested free of charge online at [L1]. This work has received funding by the EC (H2020) under grant agreements 785907 and 945539 (HBP) and the BMBF (HD-BIO-AI, 16ES1127) Link: [L1] https://kwz.me/h5E
References: [1] S. A. Aamir et al.: “A MixedSignal Structured AdEx Neuron for Accelerated Neuromorphic Cores,” IEEE Trans. Biomed. Circuits Syst., vol. 12, no. 5, pp. 1027–1037, Oct. 2018, doi: 10.1109/TBCAS.2018.2848203. [2] S. A. Aamir, Y. Stradmann, and P. Müller: “An accelerated lif neuronal network array for a largescale mixed-signal neuromorphic architecture,” on Circuits and …, 2018, [Online]. Available: https://kwz.me/h5K.
[3] S. Billaudelle et al.: “Structural plasticity on an accelerated analog neuromorphic hardware system,” Neural Netw., vol. 133, pp. 11–20, Oct. 2020, doi: 10.1016/j.neunet.2020.09.024. Please contact: Johannes Schemmel Heidelberg University, Germany, schemmel@kip.uni-heidelberg.de Björn Kindler Heidelberg University, Germany, kindler@kip.uni-heidelberg.de
BrainScaleS: Greater versatility for Neuromorphic Emulation by Andreas Baumbach (Heidelberg University, University of Bern), Sebastian Billaudelle (Heidelberg University), Virginie Sabado (University of Bern) and Mihai A. Petrovici (University of Bern, Heidelberg University) Uncovering the mechanics of neural computation in living organisms is increasingly shaping the development of brain-inspired algorithms and silicon circuits in the domain of artificial information processing. Researchers from the European Human Brain project employ the BrainScaleS spike-based neuromorphic platform to implement a variety of brain-inspired computational paradigms, from insect navigation to probabilistic generative models, demonstrating an unprecedented degree of versatility in mixed-signal neuromorphic substrates. Unlike their machine learning counterparts, biological neurons interact primarily via electrical action potentials, known as “spikes”. The second generation of the BrainScaleS neuromorphic system [1, L6] implements up to 512 such spiking neurons, which can be near-arbitrarily connected. In contrast to classical simulations, where the simulation time increases for larger systems, this form of in-silico implementation replicates the physics of neurons rather than numerically solving the associated equations, enabling what could be described as perfect scaling, with the duration of an emulation being essentially independent of the size of the model. Moreover, the electronic circuits are configured to be approximately 1000 times faster than their biological counterparts. It is this acceleration that makes BrainScaleS-2 a powerful platform for biological research and potential applications. Importantly, the chip was also designed for energy efficiency, with a total nominal power consumption of 1 W. Coupled with its emulation speed, this means energy consumption is several orders of magnitude lower than state-of-the art conventional simulaERCIM NEWS 125 April 2021
tions. These features, including the onchip learning capabilities of the second generation of the BrainScaleS architecture, have the potential to enable the widespread use of spiking neurons beyond the realm of neuroscience and into artificial intelligence (AI) applications. Here we showcase the system’s capabilities with a collection of five highly diverse emulation scenarios, from a small model of insect navigation, including the sensory apparatus and the environment, to fully fledged discriminatory and generative models of higher brain functions [1]. As a first use case, we present a model of the bee’s brain that reproduces the ability of bees to return to their nest’s location after exploring their environment for food (Figure 1). The on-chip generalpurpose processor of BrainScaleS is used to simulate the environment and to stimulate the motor neurons triggering the exploration of the environment. The network activity stores the insect’s position, enabling autonomous navigation back to its nest. The on-chip environment simulation avoids delays typically introduced by the use of coprocessors, thus fully
exploiting the speed of the accelerated platform for the experiment. In total, the emulated insect performs about 5.5 biological hours’ worth of exploration in 20 seconds. While accelerated model execution is certainly an attractive feature, it is often the learning process that is prohibitively time-consuming. Using its on-chip general-purpose processor, the BrainScaleS system offers the option of fully embedded learning, thus maximising the benefit of its accelerated neurosynaptic dynamics. Using the dedicated circuitry for spike-timing-dependent plasticity (STDP), the system can, for example, implement a neuromorphic agent playing a version of the game Pong. The general-purpose processor again simulates the game dynamics and, depending on the game outcome, provides the success signal used in the reinforcement learning. Together with the analogue STDP measurements, this reward signal allows the emulated agent to learn autonomously. The resulting system is an order of magnitude faster and three orders of magnitude more
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