ArticleFrontiers in neuroscience2015
Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.
Article in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Marine-Inspired Multimodal Sensor Fusion and Neuromorphic Processing for Autonomous Navigation in Unstructured Subaquatic Environments.Sensors (Basel, Switzerland) · 2025Review
- Balancing Energy Consumption and Detection Accuracy in Cardiovascular Disease Diagnosis: A Spiking Neural Network-Based Approach with ECG and PCG Signals.Sensors (Basel, Switzerland) · 2025Article
- Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning.Scientific reports · 2023Article
- Surrogate gradients for analog neuromorphic computing.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Review
- Unsupervised Feature Learning With Winner-Takes-All Based STDP.Frontiers in computational neuroscience · 2018Article
- Feature Representations for Neuromorphic Audio Spike Streams.Frontiers in neuroscience · 2018Article
- Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning.Frontiers in neuroscience · 2018Article
- On Practical Issues for Stochastic STDP Hardware With 1-bit Synaptic Weights.Frontiers in neuroscience · 2018Article
- Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks.Frontiers in neuroscience · 2017Article
- An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.Frontiers in neuroscience · 2017Article
- Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks.Frontiers in neuroscience · 2017Article
- Convolutional networks for fast, energy-efficient neuromorphic computing.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.International journal of molecular sciences · 2016Review
- Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.Frontiers in neuroscience · 2016Article
- Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation.Frontiers in neuroscience · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.
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