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1. Coriolis: enabling metagenomic classification on lightweight mobile devices

   https://doi.org/10.1093/bioinformatics/btad243  

   Mikalsen, Andrew J.; Zola, Jaroslaw (June 2023, Bioinformatics)

   Abstract MotivationThe introduction of portable DNA sequencers such as the Oxford Nanopore Technologies MinION has enabled real-time and in the field DNA sequencing. However, in the field sequencing is actionable only when coupled with in the field DNA classification. This poses new challenges for metagenomic software since mobile deployments are typically in remote locations with limited network connectivity and without access to capable computing devices. ResultsWe propose new strategies to enable in the field metagenomic classification on mobile devices. We first introduce a programming model for expressing metagenomic classifiers that decomposes the classification process into well-defined and manageable abstractions. The model simplifies resource management in mobile setups and enables rapid prototyping of classification algorithms. Next, we introduce the compact string B-tree, a practical data structure for indexing text in external storage, and we demonstrate its viability as a strategy to deploy massive DNA databases on memory-constrained devices. Finally, we combine both solutions into Coriolis, a metagenomic classifier designed specifically to operate on lightweight mobile devices. Through experiments with actual MinION metagenomic reads and a portable supercomputer-on-a-chip, we show that compared with the state-of-the-art solutions Coriolis offers higher throughput and lower resource consumption without sacrificing quality of classification. Availability and implementationSource code and test data are available from http://score-group.org/?id=smarten. 

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2. Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks

   Yin, Hang; Lee, John Boaz; Kong, Xiangnan; Hartvigsen, Thomas; Xie, Sihong (January 2021, Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’21))

   Spike train classification is an important problem in many areas such as healthcare and mobile sensing, where each spike train is a high-dimensional time series of binary values. Conventional re- search on spike train classification mainly focus on developing Spiking Neural Networks (SNNs) under resource-sufficient settings (e.g., on GPU servers). The neurons of the SNNs are usually densely connected in each layer. However, in many real-world applications, we often need to deploy the SNN models on resource-constrained platforms (e.g., mobile devices) to analyze high-dimensional spike train data. The high resource requirement of the densely-connected SNNs can make them hard to deploy on mobile devices. In this paper, we study the problem of energy-efficient SNNs with sparsely- connected neurons. We propose an SNN model with sparse spatiotemporal coding. Our solution is based on the re-parameterization of weights in an SNN and the application of sparsity regularization during optimization. We compare our work with the state-of-the-art SNNs and demonstrate that our sparse SNNs achieve significantly better computational efficiency on both neuromorphic and standard datasets with comparable classification accuracy. Furthermore, com- pared with densely-connected SNNs, we show that our method has a better capability of generalization on small-size datasets through extensive experiments. 

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3. Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks

   Yin, Hang; Lee, John Boaz; Kong, Xiangnan; Hartvigsen, Thomas; Xie, Sihong (January 2021, Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’21))

   null (Ed.)

   Spike train classification is an important problem in many areas such as healthcare and mobile sensing, where each spike train is a high-dimensional time series of binary values. Conventional re- search on spike train classification mainly focus on developing Spiking Neural Networks (SNNs) under resource-sufficient settings (e.g., on GPU servers). The neurons of the SNNs are usually densely connected in each layer. However, in many real-world applications, we often need to deploy the SNN models on resource-constrained platforms (e.g., mobile devices) to analyze high-dimensional spike train data. The high resource requirement of the densely-connected SNNs can make them hard to deploy on mobile devices. In this paper, we study the problem of energy-efficient SNNs with sparsely- connected neurons. We propose an SNN model with sparse spatio-temporal coding. Our solution is based on the re-parameterization of weights in an SNN and the application of sparsity regularization during optimization. We compare our work with the state-of-the-art SNNs and demonstrate that our sparse SNNs achieve significantly better computational efficiency on both neuromorphic and standard datasets with comparable classification accuracy. Furthermore, com- pared with densely-connected SNNs, we show that our method has a better capability of generalization on small-size datasets through extensive experiments. 

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4. A nanopore interface for higher bandwidth DNA computing

   https://doi.org/10.1038/s41467-022-32526-3  

   Zhang, Karen; Chen, Yuan-Jyue; Wilde, Delaney; Doroschak, Kathryn; Strauss, Karin; Ceze, Luis; Seelig, Georg; Nivala, Jeff (December 2022, Nature Communications)

   Abstract DNA has emerged as a powerful substrate for programming information processing machines at the nanoscale. Among the DNA computing primitives used today, DNA strand displacement (DSD) is arguably the most popular, with DSD-based circuit applications ranging from disease diagnostics to molecular artificial neural networks. The outputs of DSD circuits are generally read using fluorescence spectroscopy. However, due to the spectral overlap of typical small-molecule fluorescent reporters, the number of unique outputs that can be detected in parallel is limited, requiring complex optical setups or spatial isolation of reactions to make output bandwidths scalable. Here, we present a multiplexable sequencing-free readout method that enables real-time, kinetic measurement of DSD circuit activity through highly parallel, direct detection of barcoded output strands using nanopore sensor array technology (Oxford Nanopore Technologies’ MinION device). These results increase DSD output bandwidth by an order of magnitude over what is currently feasible with fluorescence spectroscopy. 

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5. SIESTA: Efficient Online Continual Learning with Sleep

   Harun, Md Yousuf; Gallardo, Jhair; Hayes, Tyler; Kemker, Ronald; Kanan, Christopher (November 2023, Transactions on machine learning research)

   In supervised continual learning, a deep neural network (DNN) is updated with an ever-growing data stream. Unlike the offline setting where data is shuffled, we cannot make any distributional assumptions about the data stream. Ideally, only one pass through the dataset is needed for computational efficiency. However, existing methods are inadequate and make many assumptions that cannot be made for real-world applications, while simultaneously failing to improve computational efficiency. In this paper, we propose a novel continual learning method, SIESTA based on wake/sleep framework for training, which is well aligned to the needs of on-device learning. The major goal of SIESTA is to advance compute efficient continual learning so that DNNs can be updated efficiently using far less time and energy. The principal innovations of SIESTA are: 1) rapid online updates using a rehearsal-free, backpropagation-free, and data-driven network update rule during its wake phase, and 2) expedited memory consolidation using a compute-restricted rehearsal policy during its sleep phase. For memory efficiency, SIESTA adapts latent rehearsal using memory indexing from REMIND. Compared to REMIND and prior arts, SIESTA is far more computationally efficient, enabling continual learning on ImageNet-1K in under 2 hours on a single GPU; moreover, in the augmentation-free setting it matches the performance of the offline learner, a milestone critical to driving adoption of continual learning in real-world applications. 

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