| 1. |
- Podobas, Artur, et al.
(författare)
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StreamBrain : An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs
- 2021
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Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM).
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Konferensbidrag (refereegranskat)abstract
- The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas. At the same time, there are other - less-known - machine learning algorithms with a mature and solid theoretical foundation whose performance remains unexplored. One such example is the brain-like Bayesian Confidence Propagation Neural Network (BCPNN). In this paper, we introduce StreamBrain - a framework that allows neural networks based on BCPNN to be practically deployed in High-Performance Computing systems. StreamBrain is a domain-specific language (DSL), similar in concept to existing machine learning (ML) frameworks, and supports backends for CPUs, GPUs, and even FPGAs. We empirically demonstrate that StreamBrain can train the well-known ML benchmark dataset MNIST within seconds, and we are the first to demonstrate BCPNN on STL-10 size networks. We also show how StreamBrain can be used to train with custom floating-point formats and illustrate the impact of using different bfloat variations on BCPNN using FPGAs.
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| 2. |
- Ravichandran, Naresh Balaji, et al.
(författare)
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Semi-supervised learning with Bayesian Confidence Propagation Neural Network
- 2021
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Ingår i: ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. - : i6doc.com publication. ; , s. 441-446
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Konferensbidrag (refereegranskat)abstract
- Learning internal representations from data using no or few labels is useful for machine learning research, as it allows using massive amounts of unlabeled data. In this work, we use the Bayesian Confidence Propagation Neural Network (BCPNN) model developed as a biologically plausible model of the cortex. Recent work has demonstrated that these networks can learn useful internal representations from data using local Bayesian-Hebbian learning rules. In this work, we show how such representations can be leveraged in a semi-supervised setting by introducing and comparing different classifiers. We also evaluate and compare such networks with other popular semi-supervised classifiers.
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| 3. |
- Stathis, Dimitrios, et al.
(författare)
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Approximate computation of post-synaptic spikes reduces bandwidth to synaptic storage in a model of cortex
- 2021
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Ingår i: PROCEEDINGS OF THE 2021 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2021). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 685-688
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Konferensbidrag (refereegranskat)abstract
- The Bayesian Confidence Propagation Neural Network (BCPNN) is a spiking model of the cortex. The synaptic weights of BCPNN are organized as matrices. They require substantial synaptic storage and a large bandwidth to it. The algorithm requires a dual access pattern to these matrices, both row-wise and column-wise, to access its synaptic weights. In this work, we exploit an algorithmic optimization that eliminates the column-wise accesses. The new computation model approximates the post-synaptic spikes computation with the use of a predictor. We have adopted this approximate computational model to improve upon the previously reported ASIC implementation, called eBrainII. We also present the error analysis of the approximation to show that it is negligible. The reduction in storage and bandwidth to the synaptic storage results in a 48% reduction in energy compared to eBrainII. The reported approximation method also applies to other neural network models based on a Hebbian learning rule.
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