Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energy consumption. However, this usually comes at the cost of reduced control over the dynamics of the emulated networks. In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate. We first convert a deep neural network trained in software to a spiking network on the BrainScaleS wafer-scale neuromorphic system, thereby enabling an acceleration factor of 10 000 compared to the biological time domain. This mapping is followed by the in-the-loop training, where in each training step, the network activity is first recorded in hardware and then used to compute the parameter updates in software via backpropagation. An essential finding is that the parameter updates do not have to be precise, but only need to approximately follow the correct gradient, which simplifies the computation of updates. Using this approach, after only several tens of iterations, the spiking network shows an accuracy close to the ideal software-emulated prototype. The presented techniques show that deep spiking networks emulated on analog neuromorphic devices can attain good computational performance despite the inherent variations of the analog substrate.Comment: 8 pages, 10 figures, submitted to IJCNN 201

Six networks on a universal neuromorphic computing substrate

In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional computers. Its configurability allows the realization of almost arbitrary network topologies and the use of widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to analog circuitry is reduced by calibration routines. An integrated development environment allows neuroscientists to operate the device without any prior knowledge of neuromorphic circuit design. As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about journal publication. Frontiers in Neuromorphic Engineering (2019

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

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

Hybrid Linux System Modeling with Mixed-Level Simulation

Dissertação de mestrado integrado em Engenharia Electrónica Industrial e ComputadoresWe live in a world where the need for computer-based systems with better performances is growing fast, and part of these systems are embedded systems. This kind of systems are everywhere around us, and we use them everyday even without noticing. Nevertheless, there are issues related to embedded systems in what comes to real-time requirements, because the failure of such systems can be harmful to the user or its environment. For this reason, a common technique to meet real-time requirements in difficult scenarios is accelerating software applications by using parallelization techniques and dedicated hardware components. This dissertations’ goal is to adopt a methodology of hardware-software co-design aided by co-simulation, making the design flow more efficient and reliable. An isolated validation does not guarantee integral system functionality, but the use of an integrated co-simulation environment allows detecting system problems before moving to the physical implementation. In this dissertation, an integrated co-simulation environment will be developed, using the Quick EMUlator (QEMU) as a tool for emulating embedded software platforms in a Linux-based environment. A SystemVerilog Direct Programming Interface (DPI) Library was developed in order to allow SystemVerilog simulators that support DPI to perform co-simulation with QEMU. A library for DLL blocks was also developed in order to allow PSIMR to communicate with QEMU. Together with QEMU, these libraries open up the possibility to co-simulate several parts of a system that includes power electronics and hardware acceleration together with an emulated embedded platform. In order to validate the functionality of the developed co-simulation environment, a demonstration application scenario was developed following a design flow that takes advantage of the mentioned simulation environment capabilities.Vivemos num mundo em que a procura por sistemas computer-based com desempenhos cada vez melhores domina o mercado. Estamos rodeados por este tipo de sistemas, usando-os todos os dias sem nos apercebermos disso, sendo grande parte deles sistemas embebidos. Ainda assim, existem problemas relacionados com os sistemas embebidos no que toca aos requisitos de tempo-real, porque uma falha destes sistemas pode ser perigosa para o utilizador ou o ambiente que o rodeia. Devido a isto, uma técnica comum para se conseguir cumprir os requisitos de tempo-real em aplicações críticas é a aceleração de aplicações de software, utilizando técnicas de paralelização e o uso de componentes de hardware dedicados. O objetivo desta dissertação é adotar uma metodologia de co-design de hardwaresoftware apoiada em co-simulação, tornando o design flow mais eficiente e fiável. Uma validação isolada não garante a funcionalidade do sistema completo, mas a utilização de um ambiente de co-simulação permite detetar problemas no sistema antes deste ser implementado na plataforma alvo. Nesta dissertação será desenvolvido um ambiente de co-simulação usando o QEMU como emulador para as plataformas de software "embebido" baseadas em Linux. Uma biblioteca para SystemVerilog DPI foi desenvolvida, que permite a co-simulação entre o QEMU e simuladores de Register-Transfer Level (RTL) que suportem SystemVerilog. Foi também desenvolvida uma biblioteca para os blocos Dynamic Link Library (DLL) do PSIMR , de modo a permitir a ligação ao QEMU. Em conjunto, as bibliotecas desenvolvidas permitem a co-simulação de diversas partes do sistema, nomeadamente do hardware de eletrónica de potência e dos aceleradores de hardware, juntamente com a plataforma embebida emulada no QEMU.Para validar as funcionalidades do ambiente de co-simulação desenvolvido, foi explorado um cenário de aplicação que tem por base esse mesmo ambiente

Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world