Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware

Stöckel A, Jenzen C, Thies M, Rückert U. Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware. Frontiers in Computational Neuroscience. 2017;11: 71.Large-scale neuromorphic hardware platforms, specialized computer systems for energy efficient simulation of spiking neural networks, are being developed around the world, for example as part of the European Human Brain Project (HBP). Due to conceptual differences, a universal performance analysis of these systems in terms of runtime, accuracy and energy efficiency is non-trivial, yet indispensable for further hard- and software development. In this paper we describe a scalable benchmark based on a spiking neural network implementation of the binary neural associative memory. We treat neuromorphic hardware and software simulators as black-boxes and execute exactly the same network description across all devices. Experiments on the HBP platforms under varying configurations of the associative memory show that the presented method allows to test the quality of the neuron model implementation, and to explain significant deviations from the expected reference output

Benchmarking and Characterization of event-based Neuromorphic Hardware

Ostrau C, Klarhorst C, Thies M, Rückert U. Benchmarking and Characterization of event-based Neuromorphic Hardware. Presented at the FastPath 2019 - International Workshop on Performance Analysis of Machine Learning Systems, Madison, Wisconsin, USA.We present the modular framework SNABSuite (Spiking Neural Architecture Benchmark Suite) for black-box benchmarking of neuromorphic hardware systems and spiking neural network software simulators. The motivation for having a coherent collection of benchmarks is twofold: first, benchmarks evaluated on different platforms provide measures for direct comparison of performance indicators (e.g. resource efficiency, quality of the result, robustness). By using the platforms as they are provided for possible end-users and evaluating selected performance indicators, benchmarks support the decision for or against a system based on use-case requirements. Second, benchmarks may reveal opportunities for effective improvements of a system and can contribute to future development. Systems like the Heidelberg BrainScaleS project, IBM TrueNorth, the Manchester SpiNNaker project or the Intel Loihi platform drive the evolution of neuromorphic hardware implementations, while comparable benchmarks and corresponding measures are still rare. We show our methodology for comparing such diverse systems by applying a modular framework, with a user- centric view based on configurable spiking neural network descriptions

Dynamical Systems in Spiking Neuromorphic Hardware

Dynamical systems are universal computers. They can perceive stimuli, remember, learn from feedback, plan sequences of actions, and coordinate complex behavioural responses. The Neural Engineering Framework (NEF) provides a general recipe to formulate models of such systems as coupled sets of nonlinear differential equations and compile them onto recurrently connected spiking neural networks – akin to a programming language for spiking models of computation. The Nengo software ecosystem supports the NEF and compiles such models onto neuromorphic hardware. In this thesis, we analyze the theory driving the success of the NEF, and expose several core principles underpinning its correctness, scalability, completeness, robustness, and extensibility. We also derive novel theoretical extensions to the framework that enable it to far more effectively leverage a wide variety of dynamics in digital hardware, and to exploit the device-level physics in analog hardware. At the same time, we propose a novel set of spiking algorithms that recruit an optimal nonlinear encoding of time, which we call the Delay Network (DN). Backpropagation across stacked layers of DNs dramatically outperforms stacked Long Short-Term Memory (LSTM) networks—a state-of-the-art deep recurrent architecture—in accuracy and training time, on a continuous-time memory task, and a chaotic time-series prediction benchmark. The basic component of this network is shown to function on state-of-the-art spiking neuromorphic hardware including Braindrop and Loihi. This implementation approaches the energy-efficiency of the human brain in the former case, and the precision of conventional computation in the latter case

Spaun 2.0: Extending the World’s Largest Functional Brain Model

Building large-scale brain models is one method used by theoretical neuroscientists to understand the way the human brain functions. Researchers typically use either a bottom-up approach, which focuses on the detailed modelling of various biological properties of the brain and places less importance on reproducing functional behaviour, or a top-down approach, which generally aim to reproduce the behaviour observed in real cognitive agents, but typically sacrifices adherence to constraints imposed by the neuro-biology. The focus of this thesis is Spaun, a large-scale brain model constructed using a combination of the bottom-up and top-down approaches to brain modelling. Spaun is currently the world’s largest functional brain model, capable of performing eight distinct cognitive tasks ranging from digit recognition to inductive reasoning. The thesis is organized to discuss three aspects of the Spaun model. First, it describes the original Spaun model, and explores how a top-down approach, known as the Semantic Pointer Architecture (SPA), has been combined with a bottom-up approach, known as the Neural Engineering Framework (NEF), to integrate six existing cognitive models into a unified cognitive model that is Spaun. Next, the thesis identifies some of the concerns with the original Spaun model, and show the modifications made to the network to remedy these issues. It also characterizes how the Spaun model was re-organized and re-implemented (to include the aforementioned modifications) as the Spaun 2.0 model. As part of the discussion of the Spaun 2.0 model, task performance results are presented that compare the original Spaun model and the re-implemented Spaun 2.0 model, demonstrating that the modifications to the Spaun 2.0 model have improved its accuracy on the working memory task, and the two induction tasks. Finally, three extensions to Spaun 2.0 are presented. These extensions take advantage of the re-organized Spaun model, giving Spaun 2.0 new capabilities – a motor system capable of adapting to unknown force fields applied to its arm; a visual system capable of processing 256×256 full-colour images; and the ability to follow general instructions. The Spaun model and architecture presented in this thesis demonstrate that by using the SPA and the NEF, it is not only possible to construct functional large-scale brain models, but to do so in a manner that supports complex extensions to the model. The final Spaun 2.0 model consists of approximately 6.6 million neurons, can perform 12 cognitive tasks, and has been demonstrated to reproduce behavioural and neurological data observed in natural cognitive agents