Neuromorphic Hardware Platforms Research Articles

Parallel Distribution of an Inner Hair Cell and Auditory Nerve Model for Real-Time Application.

This paper summarizes recent efforts in implementing a model of the ear's inner hair cell and auditory nerve on a neuromorphic hardware platform, the SpiNNaker machine. This exploits the massive parallelism of the target architecture to obtain real-time modeling of a biologically realistic number of human auditory nerve fibres. We show how this model can be integrated with additional modules that simulate previous stages of the early auditory pathway running on the same hardware architecture, thus producing a full-scale spiking auditory nerve output from a single sound stimulus. The results of the SpiNNaker implementation are shown to be comparable with a MATLAB version of the same model algorithms, while removing the inherent performance limitations associated with an increase in auditory model scale that are seen in the conventional computer simulations. Finally, we outline the potential for using this system as part of a full-scale, real-time digital model of the complete human auditory pathway on the SpiNNaker platform.

(Invited) Epitaxial Random Access Memory (epiRAM) Towards Large-Scale Synaptic Arrays

Epitaxial random access memory (epiRAM) towards large-scale synaptic arrays Shinhyun Choi, Scott Tan, Yunjo Kim, and Jeehwan Kim Massachusetts Institute of Technology Neuromorphic computing has recently emerged as a non-Von Neuman computing method. Because its analog switching ability to represent multiple synaptic weights by varying conductance in the vertical filaments formed in the switching medium, a memristor has been considered as a suitable neuromorphic hardware platform. Conventional memristors typically utilize an defective amorphous solid as a switching medium for defect-mediated formation of conducting filaments. However, the imperfection of the switching medium also causes stochastic filament formation leading to spatial and temporal variation of the devices. In this talk, we introduce a silicon-based epitaxial random access memory (epiRAM), where we precisely confined the conducting paths in the single-crystalline films resulting in unprecedented device performances. MIT’s epiRAM exhibits extremely low temporal/spatial variation, linear synaptic weight update, high on/off ratio (250 for analog/10,000 for digital), great endurance (>109), long retention time (2 days at 85oC), and self-selectivity. This will allow large-scale neural network hardware, paving the way for computing beyond the conventional von Neumann architecture.

Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware.

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.

Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model.

Connecting biologically inspired neural simulations to physical or simulated embodiments can be useful both in robotics, for the development of a new kind of bio-inspired controllers, and in neuroscience, to test detailed brain models in complete action-perception loops. The aim of this work is to develop a fully spike-based, biologically inspired mechanism for the translation of proprioceptive feedback. The translation is achieved by implementing a computational model of neural activity of type Ia and type II afferent fibers of muscle spindles, the primary source of proprioceptive information, which, in mammals is regulated through fusimotor activation and provides necessary adjustments during voluntary muscle contractions. As such, both static and dynamic γ-motoneurons activities are taken into account in the proposed model. Information from the actual proprioceptive sensors (i.e., motor encoders) is then used to simulate the spindle contraction and relaxation, and therefore drive the neural activity. To assess the feasibility of this approach, the model is implemented on the NEST spiking neural network simulator and on the SpiNNaker neuromorphic hardware platform and tested on simulated and physical robotic platforms. The results demonstrate that the model can be used in both simulated and real-time robotic applications to translate encoder values into a biologically plausible neural activity. Thus, this model provides a completely spike-based building block, suitable for neuromorphic platforms, that will enable the development of sensory-motor closed loops which could include neural simulations of areas of the central nervous system or of low-level reflexes.

Design of Ternary Neural Network With 3-D Vertical RRAM Array

Recently, 2-D cross-point array of resistive random access memory (RRAM) has been proposed for implementing the weighted sum and weight update operations to accelerate the neuro-inspired learning algorithms on chip. This paper aims to extend such 2-D cross-point array to 3-D vertical array for storing and computing the large-scale weight matrices in the neural network. Considering the fabrication and 3-D integration of analog synapses (i.e., multilevel RRAM devices) are premature at this stage, we propose using today’s available digital or binary RRAM devices for implementing a ternary neural network, which aggressively reduces the weight precision to ternary levels (+1, 0,−1) for the weighted sum in both feedforward and backward inference, while the multiple 3-D layers could serve for accumulating the small errors in a higher precision format for weight update. Compared to the 2-D implementation, the proposed 3-D vertical implementation shows larger read/write margin for weighted sum/weight update, smaller latency, and energy consumption for weight update. This paper demonstrates the attractiveness for building a monolithic 3-D neuromorphic hardware platform.

Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series

This paper presents spiking neural networks (SNNs) for remote sensing spatiotemporal analysis of image time series, which make use of the highly parallel and low-power-consuming neuromorphic hardware platforms possible. This paper illustrates this concept with the introduction of the first SNN computational model for crop yield estimation from normalized difference vegetation index image time series. It presents the development and testing of a methodological framework which utilizes the spatial accumulation of time series of Moderate Resolution Imaging Spectroradiometer 250-m resolution data and historical crop yield data to train an SNN to make timely prediction of crop yield. The research work also includes an analysis on the optimum number of features needed to optimize the results from our experimental data set. The proposed approach was applied to estimate the winter wheat ( Triticum aestivum L. ) yield in Shandong province, one of the main winter-wheat-growing regions of China. Our method was able to predict the yield around six weeks before harvest with a very high accuracy. Our methodology provided an average accuracy of 95.64%, with an average error of prediction of 0.236 t/ha and correlation coefficient of 0.801 based on a nine-feature model.

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include ‘on the fly’ new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this is presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM.

Event-driven contrastive divergence for spiking neuromorphic systems.

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.