A Binary Learning Framework for Hyperdimensional Computing

Imani, Mohsen; Messerly, John; Wu, Fan; Pi, Wang; Rosing, Tajana

doi:10.23919/date.2019.8714821

Cited by 58 publications

(39 citation statements)

References 19 publications

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“…We want to highlight that our experiments have not been performed in parallel. However, the proposed approach can be easily run in parallel on both CPU and GPU architectures due to the nature of the HD paradigm [39,40]. This would drastically speed up the procedure for building the classification model and the prediction of a class for new observations.…”

Section: Discussion Conclusion and Future Directionsmentioning

confidence: 99%

A Brain-Inspired Hyperdimensional Computing Approach for Classifying Massive DNA Methylation Data of Cancer

2020

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The recent advancements in cancer genomics have put under the spotlight DNA methylation, a genetic modification that regulates the functioning of the genome and whose modifications have an important role in tumorigenesis and tumor-suppression. Because of the high dimensionality and the enormous amount of genomic data that are produced through the last advancements in Next Generation Sequencing, it is very challenging to effectively make use of DNA methylation data in diagnostics applications, e.g., in the identification of healthy vs diseased samples. Additionally, state-of-the-art techniques are not fast enough to rapidly produce reliable results or efficient in managing those massive amounts of data. For this reason, we propose HD-classifier, an in-memory cognitive-based hyperdimensional (HD) supervised machine learning algorithm for the classification of tumor vs non tumor samples through the analysis of their DNA Methylation data. The approach takes inspiration from how the human brain is able to remember and distinguish simple and complex concepts by adopting hypervectors and no single numerical values. Exactly as the brain works, this allows for encoding complex patterns, which makes the whole architecture robust to failures and mistakes also with noisy data. We design and develop an algorithm and a software tool that is able to perform supervised classification with the HD approach. We conduct experiments on three DNA methylation datasets of different types of cancer in order to prove the validity of our algorithm, i.e., Breast Invasive Carcinoma (BRCA), Kidney renal papillary cell carcinoma (KIRP), and Thyroid carcinoma (THCA). We obtain outstanding results in terms of accuracy and computational time with a low amount of computational resources. Furthermore, we validate our approach by comparing it (i) to BIGBIOCL, a software based on Random Forest for classifying big omics datasets in distributed computing environments, (ii) to Support Vector Machine (SVM), and (iii) to Decision Tree state-of-the-art classification methods. Finally, we freely release both the datasets and the software on GitHub.

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Section: Discussion Conclusion and Future Directionsmentioning

confidence: 99%

A Brain-Inspired Hyperdimensional Computing Approach for Classifying Massive DNA Methylation Data of Cancer

2020

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“…Prior work has proposed various algorithmic and hardware innovations to tackle the computational challenges of HD. Acceleration in hardware has typically focused on FPGAs [16][17][18] or ASIC-ish accelerators [19,20]. FPGA-based implementations provide high parallelism and bit-level granularity of operations that significantly improves the effective utilization of resources and performance.…”

Section: Motivationmentioning

confidence: 99%

“…This flexibility is crucial since learning applications are heterogeneous in practice. Therefore, we here focus on an FPGA based implementation but emphasize our techniques are generic and can be integrated with ASIC- [19] and processor-based [20] implementations. As noted in the preceding section, the element-wise sum is a critical operation in the encoding pipeline.…”

Section: Motivationmentioning

confidence: 99%

SHEAR er

Khaleghi

Salamat

Thomas

et al. 2020

Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design

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Hyperdimensional computing (HD) is an emerging paradigm for machine learning based on the evidence that the brain computes on highdimensional, distributed, representations of data. The main operation of HD is encoding, which transfers the input data to hyperspace by mapping each input feature to a hypervector, followed by a bundling procedure that adds up the hypervectors to realize the encoding hypervector. The operations of HD are simple and highly parallelizable, but the large number of operations hampers the efficiency of HD in embedded domain. In this paper, we propose SHEAR , an algorithmhardware co-optimization to improve the performance and energy consumption of HD computing. We gain insight from a prudent scheme of approximating the hypervectors that, thanks to error resiliency of HD, has minimal impact on accuracy while provides high prospect for hardware optimization. Unlike previous works that generate the encoding hypervectors in full precision and then and then perform ex-post quantization, we compute the encoding hypervectors in an approximate manner that saves resources yet affords high accuracy. We also propose a novel FPGA architecture that achieves striking performance through massive parallelism with low power consumption. Moreover, we develop a software framework that enables training HD models by emulating the proposed approximate encodings. The FPGA implementation of SHEAR achieves an average throughput boost of 104,904× (15.7×) and energy savings of up to 56,044× (301×) compared to state-of-the-art encoding methods implemented on Raspberry Pi 3 (GeForce GTX 1080 Ti) using practical machine learning datasets.

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“…Input data can have different representations, thus there are different encoding modules to map data to high dimensional space. For example, work in [16,22] proposed encoding methods to map feature vectors to high dimensional space. Work in [23] encodes text-like data using the idea of random indexing.…”

Section: Encoding Modulementioning

confidence: 99%

Fach

Imani

Salamat

Gupta

et al. 2019

Proceedings of the 24th Asia and South Pacific Design Automation Conference

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Brain-inspired hyperdimensional (HD) computing explores computing with hypervectors for the emulation of cognition as an alternative to computing with numbers. In HD, input symbols are mapped to a hypervector and an assoc iative search is performed for reasoning and classification. An associative memory, which finds the closest match between a set of learned hypervectors and a query hypervector, uses simple Hamming distance metric for similarity check. However, we observe that, in order to provide acceptable classification accuracy HD needs to store non-binarized model in associative memory and uses costly similarity metrics such as cosine to perform a reasoning task. This makes the HD computationally expensive when it is used for realistic classification problems. In this paper, we propose a FPGA-based acceleration of HD (FACH) which significantly improves the computation efficiency by removing majority of multiplications during the reasoning task. FACH identifies representative values in each class hypervector using clustering algorithm. Then, it creates a new HD model with hardware-friendly operations, and accordingly propose an FPGA-based implementation to accelerate such tasks. Our evaluations on several classification problems show that FACH can provide 5.9× energy efficiency improvement and 5.1× speedup as compared to baseline FPGA-based implementation, while ensuring the same quality of classification. CCS CONCEPTS • Computing methodologies → Machine learning approaches; Supervised learning;

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A Binary Learning Framework for Hyperdimensional Computing

Cited by 58 publications

References 19 publications

A Brain-Inspired Hyperdimensional Computing Approach for Classifying Massive DNA Methylation Data of Cancer

A Brain-Inspired Hyperdimensional Computing Approach for Classifying Massive DNA Methylation Data of Cancer

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