From Clean Room to Machine Room: Commissioning of the First-Generation BrainScaleS Wafer-Scale Neuromorphic System

The first-generation of BrainScaleS, also referred to as BrainScaleS-1, is a neuromorphic system for emulating large-scale networks of spiking neurons. Following a "physical modeling" principle, its VLSI circuits are designed to emulate the dynamics of biological examples: analog circuits implement neurons and synapses with time constants that arise from their electronic components' intrinsic properties. It operates in continuous time, with dynamics typically matching an acceleration factor of 10000 compared to the biological regime. A fault-tolerant design allows it to achieve wafer-scale integration despite unavoidable analog variability and component failures. In this paper, we present the commissioning process of a BrainScaleS-1 wafer module, providing a short description of the system's physical components, illustrating the steps taken during its assembly and the measures taken to operate it. Furthermore, we reflect on the system's development process and the lessons learned to conclude with a demonstration of its functionality by emulating a wafer-scale synchronous firing chain, the largest spiking network emulation ran with analog components and individual synapses to date.

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.

Demonstrating Analog Inference on the BrainScaleS-2 Mobile System

We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset. The analog network core of the ASIC is utilized to perform the multiply-accumulate operations of a convolutional deep neural network. We measure a total energy consumption of 192uJ for the ASIC and achieve a classification time of 276us per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of 93.7(7)% at 14.0(10)% false positives. The system is directly applicable to edge inference applications due to its small size, power envelope and flexible I/O capabilities. Possible future applications can furthermore combine conventional machine learning layers with online-learning in spiking neural networks on a single BrainScaleS-2 ASIC. The system has successfully participated and proven to operate reliably in the independently judged competition "Pilotinnovationswettbewerb 'Energieeffizientes KI-System'" of the German Federal Ministry of Education and Research (BMBF).

Inference with Artificial Neural Networks on Analog Neuromorphic Hardware

The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neural networks, the system can also operate in a vector-matrix multiplication and accumulation mode for artificial neural networks. Analog multiplication is carried out in the synapse circuits, while the results are accumulated on the neurons' membrane capacitors. Designed as an analog, in-memory computing device, it promises high energy efficiency. Fixed-pattern noise and trial-to-trial variations, however, require the implemented networks to cope with a certain level of perturbations. Further limitations are imposed by the digital resolution of the input values (5 bit), matrix weights (6 bit) and resulting neuron activations (8 bit). In this paper, we discuss BrainScaleS-2 as an analog inference accelerator and present calibration as well as optimization strategies, highlighting the advantages of training with hardware in the loop. Among other benchmarks, we classify the MNIST handwritten digits dataset using a two-dimensional convolution and two dense layers. We reach 98.0% test accuracy, closely matching the performance of the same network evaluated in software.

Inference with Artificial Neural Networks on the Analog BrainScaleS-2 Hardware

The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neural networks, the system can also operate in a vector-matrix multiplication and accumulation mode for artificial neural networks. Analog multiplication is then carried out in the synapse circuits, while the results are accumulated on the neurons' membrane capacitors. Designed as an analog, in-memory computing device, it promises high energy efficiency. Fixed-pattern noise and trial-to-trial variations, however, require the implemented networks to cope with a certain level of perturbations. Further limitations are imposed by the digital resolution of the input values (5-bit), matrix weights (6-bit) and resulting neuron activations (8-bit). In this paper, we discuss BrainScaleS-2 as an analog inference accelerator and present calibration as well as optimization strategies, highlighting the advantages of training with hardware in the loop. Among other benchmarks, we classify the MNIST handwritten digits dataset using a two-dimensional convolution and two dense layers. We reach 98.0 % test accuracy, closely matching the performance of the same network evaluated in software.

The Operating System of the Neuromorphic BrainScaleS-1 System

BrainScaleS-1 is a wafer-scale mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing. The BrainScaleS Operating System (BrainScaleS OS) is a software stack giving users the possibility to emulate networks described in the high-level network description language PyNN with minimal knowledge of the system. At the same time, expert usage is facilitated by allowing to hook into the system at any depth of the stack. We present operation and development methodologies implemented for the BrainScaleS-1 neuromorphic architecture and walk through the individual components of BrainScaleS OS constituting the software stack for BrainScaleS-1 platform operation.

Generative models on accelerated neuromorphic hardware

The traditional von Neumann computer architecture faces serious obstacles, both in terms of miniaturization and in terms of heat production, with increasing performance. Artificial neural (neuromorphic) substrates represent an alternative approach to tackle this challenge. A special subset of these systems follow the principle of "physical modeling" as they directly use the physical properties of the underlying substrate to realize computation with analog components. While these systems are potentially faster and/or more energy efficient than conventional computers, they require robust models that can cope with their inherent limitations in terms of controllability and range of parameters. A natural source of inspiration for robust models is neuroscience as the brain faces similar challenges. It has been recently suggested that sampling with the spiking dynamics of neurons is potentially suitable both as a generative and a discriminative model for artificial neural substrates. In this work we present the implementation of sampling with leaky integrate-and-fire neurons on the BrainScaleS physical model system. We prove the sampling property of the network and demonstrate its applicability to high-dimensional datasets. The required stochasticity is provided by a spiking random network on the same substrate. This allows the system to run in a self-contained fashion without external stochastic input from the host environment. The implementation provides a basis as a building block in large-scale biologically relevant emulations, as a fast approximate sampler or as a framework to realize on-chip learning on (future generations of) accelerated spiking neuromorphic hardware. Our work contributes to the development of robust computation on physical model systems.