Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

Thakur, Chetan Singh; Molin, Jamal Lottier; Cauwenberghs, Gert; Indiveri, Giacomo; Kumar, Kundan; Qiao, Ning; Schemmel, Johannes; Wang, Runchun; Chicca, Elisabetta; Hasler, Jennifer; Seo, Jae-sun; Yu, Shimeng; Cao, Yu; Schaik, André van; Etienne‐Cummings, Ralph

doi:10.3389/fnins.2018.00891

Cited by 217 publications

(158 citation statements)

References 129 publications

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“…The development of autonomous agents capable of learning by interacting with an environment has seen a tremendous surge of interest over the past decade [3,4,6]. Similarly, the design of neuromorphic applicationspecific hardware has attracted massive attention due to its enhanced computational capabilities in terms of speed and energy efficiency [15]. In this work, we propose a blueprint for an application-specific integrated photonic architecture capable of solving problems in RL.…”

Section: Discussionmentioning

confidence: 99%

“…One of the key ingredients for this advancement was the ultra-large-scale integration [14], which led to the massive capabilities of current portable devices. Meanwhile, in the wake of this technological progress, neuromorphic engineering [15] was developed to mimic neuro-biological systems on application-specific integrated circuits (ASIC) [16]. Their improved performance is rooted in the parallelized operation and in the absence of a clear separation between memory and processing unit, which eliminates off-circuit data transfers.…”

Section: Introductionmentioning

confidence: 99%

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Photonic architecture for reinforcement learning

et al. 2020

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The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in quantum optical experiments appears to be within the reach of near-term technology.OPEN ACCESS RECEIVED promising features as compared to electronic processors [27][28][29][30]. For instance, nanosecond-scale routing and reconfigurability have already been demonstrated [31][32][33], while encoding information in photons enables decision-making at the speed of light, only limited by the generation and detection rates. Moreover, the use of phase-change materials for in-memory information processing [34] promises to enhance the energy efficiency, since their properties can be modified without continuous external intervention [35,36]. Importantly, since the architecture uses single photons, decision-making is fueled by genuine quantum randomness. This feature marks a fundamental departure from pseudorandom number generation in conventional devices. (ii) The second contribution is the development of a specific variant of PS based on binary decision trees (tree-PS, or t-PS for short), which is closely connected to the standard PS and suitable for the implementation on a photonic circuit. Furthermore, we discuss how this variant enables key features of artificial intelligence, namely abstraction and generalization [37,38].The Article is structured as follows. In section 2 we summarize the theoretical framework of RL, exemplified by three common approaches: SARSA, Q-learning, and PS. In section 3 we describe the blueprint for a fully integrated, photonic RL agent. We then numerically investigate its performance within two standard RL tasks and under realistic experimental imperfections in section 4. Finally, in section 5 we discuss promising features of this architecture within the context of t-PS.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Photonic architecture for reinforcement learning

et al. 2020

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“…Currently, SNN applications span different fields, including computational neuroscience (Markram, 2012;Melozzi et al, 2017), and very recently, they were used for sensory encoding in hand prosthesis for amputees (Osborn et al, 2018;Valle et al, 2018). SNNs can be simulated in software (Goodman and Brette, 2009) and/or neuromorphic hardware (Thakur et al, 2018). As time and energy consumption are fundamental in neuroprosthetic applications for translational purposes, the use of hardwarebased computing systems becomes mandatory.…”

Section: Discussionmentioning

confidence: 99%

A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks

et al. 2019

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Summary Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care.

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“…These restrictions put some doubt on whether complex learning mechanisms, as the one considered here, can be implemented exactly. Also, exact implementation of the synaptic sampling model seems infeasible on neuromorphic hardwares with configurable (but not programmable) plasticity, like ROLLS [64], ODIN [65] and TITAN [66] (see [67] and [68] for reviews). However, it might be possible to realize simplified, approximate, versions of synaptic sampling on these neuromorphic platforms.…”

Section: Comparison With Other Neuromorphic Platformsmentioning

confidence: 99%

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype

Yan

Kappel

Neumärker

et al. 2019

IEEE Trans. Biomed. Circuits Syst.

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Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since brain-inspired algorithms often make extensive use of complex functions such as random number generators, that are expensive to compute on standard general purpose hardware. The prototype chip of the 2nd generation SpiNNaker system is designed to overcome this problem. Low-power ARM processors equipped with a random number generator and an exponential function accelerator enable the efficient execution of brain-inspired algorithms. We implement the recently introduced reward-based synaptic sampling model that employs structural plasticity to learn a function or task. The numerical simulation of the model requires to update the synapse variables in each time step including an explorative random term. To the best of our knowledge, this is the most complex synapse model implemented so far on the SpiNNaker system. By making efficient use of the hardware accelerators and numerical optimizations the computation time of one plasticity update is reduced by a factor of 2. This, combined with fitting the model into to the local SRAM, leads to 62% energy reduction compared to the case without accelerators and the use of external DRAM. The model implementation is integrated into the SpiNNaker software framework allowing for scalability onto larger systems. The hardware-software system presented in this work paves the way for power-efficient mobile and biomedical applications with biologically plausible brain-inspired algorithms.

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Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

Cited by 217 publications

References 129 publications

Photonic architecture for reinforcement learning

Photonic architecture for reinforcement learning

A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype

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