scholarly journals Deep Learning Accelerators: A Case Study with MAESTRO

2020 ◽  
Author(s):  
Hamidreza Bolhasani ◽  
Somayyeh Jafarali Jassbi
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Performance Indicators ◽  
Learning Task ◽  
High Accuracy ◽  
Spatio Temporal ◽  
Almost All ◽  
L1 And L2 ◽  
Accuracy Speed

Abstract In recent years, deep learning has become one of the most important topics in computer sciences. Deep learning is a growing trend in the edge of technology and its applications are now seen in many aspects of our life such as object detection, speech recognition, natural language processing, etc. Currently, almost all major sciences and technologies are benefiting from the advantages of deep learning such as high accuracy, speed and flexibility. Therefore, any efforts in improving performance of related techniques is valuable. Deep learning accelerators are considered as hardware architecture, which are designed and optimized for increasing speed, efficiency and accuracy of computers that are running deep learning algorithms. In this paper, after reviewing some backgrounds on deep learning, a well-known accelerator architecture named MAERI (Multiply-Accumulate Engine with Reconfigurable interconnects) is investigated. Performance of a deep learning task is measured and compared in two different data flow strategies: NLR (No Local Reuse) and NVDLA (NVIDIA Deep Learning Accelerator), using an open source tool called MAESTRO (Modeling Accelerator Efficiency via Spatio-Temporal Resource Occupancy). Measured performance indicators of novel optimized architecture, NVDLA shows higher L1 and L2 computation reuse, and lower total runtime (cycles) in comparison to the other one.

scholarly journals Deep Learning Accelerators: A Case Study with MAESTRO

2020 ◽  
Author(s):  
Hamidreza Bolhasani ◽  
Somayyeh Jafarali Jassbi
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Performance Indicators ◽  
Learning Task ◽  
High Accuracy ◽  
Spatio Temporal ◽  
Almost All ◽  
L1 And L2 ◽  
Accuracy Speed

Abstract In recent years, deep learning has become one of the most important topics in computer sciences. Deep learning is a growing trend in the edge of technology and its applications are now seen in many aspects of our life such as object detection, speech recognition, natural language processing, etc. Currently, almost all major sciences and technologies are benefiting from the advantages of deep learning such as high accuracy, speed and flexibility. Therefore, any efforts in improving performance of related techniques is valuable. Deep learning accelerators are considered as hardware architecture, which are designed and optimized for increasing speed, efficiency and accuracy of computers that are running deep learning algorithms. In this paper, after reviewing some backgrounds on deep learning, a well-known accelerator architecture named MAERI (Multiply-Accumulate Engine with Reconfigurable interconnects) is investigated. Performance of a deep learning task is measured and compared in two different data flow strategies: NLR (No Local Reuse) and NVDLA (NVIDIA Deep Learning Accelerator), using an open source tool called MAESTRO (Modeling Accelerator Efficiency via Spatio-Temporal Resource Occupancy). Measured performance indicators of novel optimized architecture, NVDLA shows higher L1 and L2 computation reuse, and lower total runtime (cycles) in comparison to the other one.

scholarly journals Deep learning accelerators: a case study with MAESTRO

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Hamidreza Bolhasani ◽  
Somayyeh Jafarali Jassbi
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Performance Indicators ◽  
Learning Task ◽  
High Accuracy ◽  
Spatio Temporal ◽  
Almost All ◽  
L1 And L2 ◽  
Accuracy Speed

AbstractIn recent years, deep learning has become one of the most important topics in computer sciences. Deep learning is a growing trend in the edge of technology and its applications are now seen in many aspects of our life such as object detection, speech recognition, natural language processing, etc. Currently, almost all major sciences and technologies are benefiting from the advantages of deep learning such as high accuracy, speed and flexibility. Therefore, any efforts in improving performance of related techniques is valuable. Deep learning accelerators are considered as hardware architecture, which are designed and optimized for increasing speed, efficiency and accuracy of computers that are running deep learning algorithms. In this paper, after reviewing some backgrounds on deep learning, a well-known accelerator architecture named MAERI (Multiply-Accumulate Engine with Reconfigurable interconnects) is investigated. Performance of a deep learning task is measured and compared in two different data flow strategies: NLR (No Local Reuse) and NVDLA (NVIDIA Deep Learning Accelerator), using an open source tool called MAESTRO (Modeling Accelerator Efficiency via Spatio-Temporal Resource Occupancy). Measured performance indicators of novel optimized architecture, NVDLA shows higher L1 and L2 computation reuse, and lower total runtime (cycles) in comparison to the other one.

scholarly journals Deep Learning Accelerators: A Case Study

2020 ◽  
Author(s):  
Hamidreza Bolhasani ◽  
Somayyeh Jafarali Jassbi
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Performance Indicators ◽  
Learning Task ◽  
High Accuracy ◽  
The Other ◽  
Almost All ◽  
L1 And L2 ◽  
Accuracy Speed

Abstract In the recent years, deep learning has become one of the most important topics in computer science. Deep learning is a growing trend in the edge of technology and its applications are now seen in many aspects of our life such as object detection, speech recognition, natural language processing, etc. Currently, almost all major sciences and technologies are benefiting from the advantages of deep learning such as high accuracy, speed and flexibility. Therefore, any efforts for improving performance of related techniques is valuable. Deep learning accelerators are considered as hardware architecture, which are designed and optimized for increasing the speed, efficiency and accuracy of computers that are running deep learning algorithms. In this paper, after reviewing some backgrounds about deep learning, a well-known accelerator architecture named MAERI is investigated. By using an open source tool called MAESTRO, the performance of a deep learning task is measured and compared on two different data flow strategies: NLR and NVDLA. Measured performance indicators of novel optimized architecture, NVDLA shows higher L1 and L2 computation reuse and lower total runtime (cycles) in comparison to the other one.

Senti-BAS: A BERT-based model with sentiment computing for happiness research (Preprint)

2021 ◽  
Author(s):  
Zeyuan Zeng ◽  
Yijia Zhang ◽  
Liang Yang ◽  
Hongfei Lin
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
High Accuracy ◽  
Language Models ◽  
Fine Grained ◽  
Label Information ◽  
Common Criterion ◽  
Text Content ◽  
Sentiment Computing

BACKGROUND Happiness becomes a rising topic that we all care about recently. It can be described in various forms. For the text content, it is an interesting subject that we can do research on happiness by utilizing natural language processing (NLP) methods. OBJECTIVE As an abstract and complicated emotion, there is no common criterion to measure and describe happiness. Therefore, researchers are creating different models to study and measure happiness. METHODS In this paper, we present a deep-learning based model called Senti-BAS (BERT embedded Bi-LSTM with self-Attention mechanism along with the Sentiment computing). RESULTS Given a sentence that describes how a person felt happiness recently, the model can classify the happiness scenario in the sentence with two topics: was it controlled by the author (label ‘agency’), and was it involving other people (label ‘social’). Besides language models, we employ the label information through sentiment computing based on lexicon. CONCLUSIONS The model performs with a high accuracy on both ‘agency’ and ‘social’ labels, and we also make comparisons with several popular embedding models like Elmo, GPT. Depending on our work, we can study the happiness at a more fine-grained level.

scholarly journals Cognitive Deficit of Deep Learning in Numerosity

2019 ◽  
Vol 33 ◽  
pp. 1303-1310
Author(s):  
Xiaolin Wu ◽  
Xi Zhang ◽  
Xiao Shu
Keyword(s):  
Deep Learning ◽  
Cognitive Deficit ◽  
Visual Representations ◽  
High Accuracy ◽  
Black Box ◽  
Cognitive Computing ◽  
Minimum Level ◽  
Test Bed ◽  
Abstract Notion

Subitizing, or the sense of small natural numbers, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic skills, language or arithmetic. Given successes of deep learning (DL) in tasks of visual intelligence and given the primitivity of number sense, a tantalizing question is whether DL can comprehend numbers and perform subitizing. But somewhat disappointingly, extensive experiments of the type of cognitive psychology demonstrate that the examples-driven black box DL cannot see through superficial variations in visual representations and distill the abstract notion of natural number, a task that children perform with high accuracy and confidence. The failure is apparently due to the learning method not the CNN computational machinery itself. A recurrent neural network capable of subitizing does exist, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels. Also, we investigate, using subitizing as a test bed, the ways to aid the black box DL by cognitive priors derived from human insight. Our findings are mixed and interesting, pointing to both cognitive deficit of pure DL, and some measured successes of boosting DL by predetermined cognitive implements. This case study of DL in cognitive computing is meaningful for visual numerosity represents a minimum level of human intelligence.

Business Applications of Deep Learning

2020 ◽  
pp. 942-964
Author(s):  
Armando Vieira
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Video Processing ◽  
Autonomous Vehicles ◽  
Video Annotation ◽  
Business Applications ◽  
Personal Assistants ◽  
Efficient Learning ◽  
High Level ◽  
Almost All

Deep Learning (DL) took Artificial Intelligence (AI) by storm and has infiltrated into business at an unprecedented rate. Access to vast amounts of data extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges. DL is the cornerstone technology behind products for image recognition and video annotation, voice recognition, personal assistants, automated translation and autonomous vehicles. DL works similarly to the brain by extracting high-level, complex abstractions from data in a hierarchical and discriminative or generative way. The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. In this chapter, I present the most significant algorithms and applications, including Natural Language Processing (NLP), image and video processing and finance.

scholarly journals A Highly Generalizable Natural Language Processing Algorithm for the Diagnosis of Pulmonary Embolism from Radiology Reports

2020 ◽  
Author(s):  
Jacob Johnson ◽  
Grace Qiu ◽  
Christine Lamoureux ◽  
Jennifer Ngo ◽  
Lawrence Ngo
Keyword(s):  
Pulmonary Embolism ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Sample Size ◽  
Language Processing ◽  
High Accuracy ◽  
Free Text ◽  
Radiology Reports ◽  
Natural Language Processing Algorithm

AbstractThough sophisticated algorithms have been developed for the classification of free-text radiology reports for pulmonary embolism (PE), their overall generalizability remains unvalidated given limitations in sample size and data homogeneity. We developed and validated a highly generalizable deep-learning based NLP algorithm for this purpose with data sourced from over 2,000 hospital sites and 500 radiologists. The algorithm achieved an AUCROC of 0.995 on chest angiography studies and 0.994 on non-angiography studies for the presence or absence of PE. The high accuracy achieved on this large and heterogeneous dataset allows for the possibility of application in large multi-center radiology practices as well as for deployment at novel sites without significant degradation in performance.

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