The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform
Zili Shao - One of the best experts on this subject based on the ideXlab platform.
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A Highly Parallelized PIM-Based Accelerator for Transaction-Based Blockchain in IoT Environment
IEEE Internet of Things Journal, 2020Co-Authors: Qian Wang, Zhaoyan Shen, Zhiping Jia, Mengying Zhao, Tianyu Wang, Renhai Chen, Zili ShaoAbstract:Blockchain has gained a lot of attention from both academia and industry. However, traditional standard blockchains, such as Bitcoin and Ethereum, suffer from low throughput, high computation overhead, and large transaction fee, which is not suitable for Internet of Things (IoT) transactions. Recently, transaction-based approaches, such as Tangle structure which is based on a directed acyclic graph (DAG), have emerged to solve blockchain scalability issues for IoT environment. In transaction-based blockchain, for a transaction, namely, a node, to be attached to the Tangle, it needs to verify two other transactions. However, with the Tangle expanding, this attaching process consumes huge computational resources and energy, which severely limits the performance of the transaction-based blockchain. In this article, we present Re-Tangle, a highly parallelized processing-in-memory (PIM)-based accelerator for transaction-based blockchain. Re-Tangle is composed of a random walking Module, a transaction Validation Module, and a PoW Module, to improve the Tangle system performance. These Modules transfer Tangle functions, such as fast exponentiation and modular, into ReRAM-based logic analog computation units. In the random walking Module, Re-Tangle maintains an exponentiation table to reduce its design complexity and improve its computation efficiency. In the transaction Validation Module, Re-Tangle further proposes a highly parallel modular unit to accelerate the Validation of different tags in a transaction. In the PoW Module, we decompose the Curl hash function into basic logic OR, AND, SHIFT, and XOR operations, and map these logic operations to ReRAM crossbars in parallel to accelerate the working process. The experimental results show that Re-Tangle distinguishes itself from other architectures with significant performance improvement and energy saving. The throughput of Re-Tangle is about $22.4\times $ and $2.38\times $ higher compared with CPU and GPU, respectively, and the energy consumption of Re-Tangle is $83.5\times $ and $5.77\times $ less for equal workload.
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ICCAD - Re-Tangle: A ReRAM-based Processing-in-Memory Architecture for Transaction-based Blockchain
2019 IEEE ACM International Conference on Computer-Aided Design (ICCAD), 2019Co-Authors: Qian Wang, Wang Tianyu, Zhaoyan Shen, Zhiping Jia, Mengying Zhao, Zili ShaoAbstract:Blockchain has gained a lot of attentions from both academic and industry. Transaction-based approaches such like Tangle structure, which is based on a DAG (Directed Acyclic Graph), are emerging to solve the blockchain scalability issues for IoT environment. In transaction-based blockchain, for a transaction, namely a node, to be attached to the Tangle, it needs to verify two other transactions. However, with the Tangle expanding, this attaching process consumes huge computational resources and energy, which severely limits the performance of the transaction-based blockchain. In this paper, we present Re-Tangle, a novel transaction-based blockchain acceleration architecture that explores the opportunity of performing massive parallel operations with low hardware and energy cost. Re-Tangle consists of a random walking Module and a transaction Validation Module, which transfer Tangle functions into ReRAM-based logic analog computation units. In the random walking Module, Re-Tangle maintains a exponentiation translator to reduce its design complexity and improve its computation efficiency for exponentiation. In the transaction Validation Module, Re-Tangle further proposes a highly parallel modular unit to accelerate the Validation of different tags in a transaction. The experience results show that Re-Tangle distinguishes itself from other architectures, with significant performance improvement and energy saving. The throughput of Re-Tangle is about 19.4× and 2.13× higher compared with CPU and GPU, respectively, and the energy consumption of Re-Tangle is 63.35 × and 4.92 × less.
Xavier Troussard - One of the best experts on this subject based on the ideXlab platform.
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Evaluation and optimization of the extended information process unit (E-IPU) Validation Module integrating the sysmex flag systems and the recommendations of the French-speaking cellular hematology group (GFHC)
Scandinavian journal of clinical and laboratory investigation, 2016Co-Authors: Edouard Cornet, François Mullier, Noémie Despas, Hugues Jacqmin, Carole Geara, Marouane Boubaya, Bernard Chatelain, Xavier TroussardAbstract:The French-Speaking Cellular Haematology Group (GFHC) recently published criteria for microscopic analysis of a blood smears when a hemogram is requested. In order to evaluate and improve these recommendations using an XN (Sysmex) analyzer, we assessed 31,836 samples categorized into two sub-groups of patients either receiving or not receiving care in the clinical hematology/oncology departments of two university hospitals. By combining the manufacturer's recommendations and the GFHC recommendations, 21.3% of samples had a positive review flag in phase 1 of our study (17,991 samples). In phase 2 (13,845 samples), increasing the immature granulocytes (IG) percentage from 5-10% as a review trigger threshold, and ignoring slides with isolated flags 'PLT HIGH' (thrombocytosis) or 'MCV LOW' (microcytosis) or 'Blast/Abn Lymph and Atypical Lymph' (blast cells/abnormal lymphocytes and atypical lymphocytes) (in the absence of abnormal cells on a previous blood smear within 72 h), enabled us to significantly reduce the number of slides reviewed from 21.3-15.0% (p
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evaluation and optimization of the extended information process unit e ipu Validation Module integrating the sysmex flag systems and the recommendations of the french speaking cellular hematology group gfhc
Scandinavian Journal of Clinical & Laboratory Investigation, 2016Co-Authors: Edouard Cornet, François Mullier, Noémie Despas, Hugues Jacqmin, Carole Geara, Marouane Boubaya, Bernard Chatelain, Xavier TroussardAbstract:The French-Speaking Cellular Haematology Group (GFHC) recently published criteria for microscopic analysis of a blood smears when a hemogram is requested. In order to evaluate and improve these recommendations using an XN (Sysmex) analyzer, we assessed 31,836 samples categorized into two sub-groups of patients either receiving or not receiving care in the clinical hematology/oncology departments of two university hospitals. By combining the manufacturer's recommendations and the GFHC recommendations, 21.3% of samples had a positive review flag in phase 1 of our study (17,991 samples). In phase 2 (13,845 samples), increasing the immature granulocytes (IG) percentage from 5-10% as a review trigger threshold, and ignoring slides with isolated flags 'PLT HIGH' (thrombocytosis) or 'MCV LOW' (microcytosis) or 'Blast/Abn Lymph and Atypical Lymph' (blast cells/abnormal lymphocytes and atypical lymphocytes) (in the absence of abnormal cells on a previous blood smear within 72 h), enabled us to significantly reduce the number of slides reviewed from 21.3-15.0% (p < 0.0001), without loss of clinical value. This decrease occurred in both sub-groups (hematology 48.7-38.0%, non-hematology 18.3-11.7%, p < 0.0001). In conclusion, the application of the GFHC criteria adapted to XN analyzers has enabled us to optimize the hematology laboratory processes, and thus reduce the production costs and the turnaround time of hemogram results.
Qian Wang - One of the best experts on this subject based on the ideXlab platform.
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A Highly Parallelized PIM-Based Accelerator for Transaction-Based Blockchain in IoT Environment
IEEE Internet of Things Journal, 2020Co-Authors: Qian Wang, Zhaoyan Shen, Zhiping Jia, Mengying Zhao, Tianyu Wang, Renhai Chen, Zili ShaoAbstract:Blockchain has gained a lot of attention from both academia and industry. However, traditional standard blockchains, such as Bitcoin and Ethereum, suffer from low throughput, high computation overhead, and large transaction fee, which is not suitable for Internet of Things (IoT) transactions. Recently, transaction-based approaches, such as Tangle structure which is based on a directed acyclic graph (DAG), have emerged to solve blockchain scalability issues for IoT environment. In transaction-based blockchain, for a transaction, namely, a node, to be attached to the Tangle, it needs to verify two other transactions. However, with the Tangle expanding, this attaching process consumes huge computational resources and energy, which severely limits the performance of the transaction-based blockchain. In this article, we present Re-Tangle, a highly parallelized processing-in-memory (PIM)-based accelerator for transaction-based blockchain. Re-Tangle is composed of a random walking Module, a transaction Validation Module, and a PoW Module, to improve the Tangle system performance. These Modules transfer Tangle functions, such as fast exponentiation and modular, into ReRAM-based logic analog computation units. In the random walking Module, Re-Tangle maintains an exponentiation table to reduce its design complexity and improve its computation efficiency. In the transaction Validation Module, Re-Tangle further proposes a highly parallel modular unit to accelerate the Validation of different tags in a transaction. In the PoW Module, we decompose the Curl hash function into basic logic OR, AND, SHIFT, and XOR operations, and map these logic operations to ReRAM crossbars in parallel to accelerate the working process. The experimental results show that Re-Tangle distinguishes itself from other architectures with significant performance improvement and energy saving. The throughput of Re-Tangle is about $22.4\times $ and $2.38\times $ higher compared with CPU and GPU, respectively, and the energy consumption of Re-Tangle is $83.5\times $ and $5.77\times $ less for equal workload.
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ICCAD - Re-Tangle: A ReRAM-based Processing-in-Memory Architecture for Transaction-based Blockchain
2019 IEEE ACM International Conference on Computer-Aided Design (ICCAD), 2019Co-Authors: Qian Wang, Wang Tianyu, Zhaoyan Shen, Zhiping Jia, Mengying Zhao, Zili ShaoAbstract:Blockchain has gained a lot of attentions from both academic and industry. Transaction-based approaches such like Tangle structure, which is based on a DAG (Directed Acyclic Graph), are emerging to solve the blockchain scalability issues for IoT environment. In transaction-based blockchain, for a transaction, namely a node, to be attached to the Tangle, it needs to verify two other transactions. However, with the Tangle expanding, this attaching process consumes huge computational resources and energy, which severely limits the performance of the transaction-based blockchain. In this paper, we present Re-Tangle, a novel transaction-based blockchain acceleration architecture that explores the opportunity of performing massive parallel operations with low hardware and energy cost. Re-Tangle consists of a random walking Module and a transaction Validation Module, which transfer Tangle functions into ReRAM-based logic analog computation units. In the random walking Module, Re-Tangle maintains a exponentiation translator to reduce its design complexity and improve its computation efficiency for exponentiation. In the transaction Validation Module, Re-Tangle further proposes a highly parallel modular unit to accelerate the Validation of different tags in a transaction. The experience results show that Re-Tangle distinguishes itself from other architectures, with significant performance improvement and energy saving. The throughput of Re-Tangle is about 19.4× and 2.13× higher compared with CPU and GPU, respectively, and the energy consumption of Re-Tangle is 63.35 × and 4.92 × less.
Edouard Cornet - One of the best experts on this subject based on the ideXlab platform.
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Evaluation and optimization of the extended information process unit (E-IPU) Validation Module integrating the sysmex flag systems and the recommendations of the French-speaking cellular hematology group (GFHC)
Scandinavian journal of clinical and laboratory investigation, 2016Co-Authors: Edouard Cornet, François Mullier, Noémie Despas, Hugues Jacqmin, Carole Geara, Marouane Boubaya, Bernard Chatelain, Xavier TroussardAbstract:The French-Speaking Cellular Haematology Group (GFHC) recently published criteria for microscopic analysis of a blood smears when a hemogram is requested. In order to evaluate and improve these recommendations using an XN (Sysmex) analyzer, we assessed 31,836 samples categorized into two sub-groups of patients either receiving or not receiving care in the clinical hematology/oncology departments of two university hospitals. By combining the manufacturer's recommendations and the GFHC recommendations, 21.3% of samples had a positive review flag in phase 1 of our study (17,991 samples). In phase 2 (13,845 samples), increasing the immature granulocytes (IG) percentage from 5-10% as a review trigger threshold, and ignoring slides with isolated flags 'PLT HIGH' (thrombocytosis) or 'MCV LOW' (microcytosis) or 'Blast/Abn Lymph and Atypical Lymph' (blast cells/abnormal lymphocytes and atypical lymphocytes) (in the absence of abnormal cells on a previous blood smear within 72 h), enabled us to significantly reduce the number of slides reviewed from 21.3-15.0% (p
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evaluation and optimization of the extended information process unit e ipu Validation Module integrating the sysmex flag systems and the recommendations of the french speaking cellular hematology group gfhc
Scandinavian Journal of Clinical & Laboratory Investigation, 2016Co-Authors: Edouard Cornet, François Mullier, Noémie Despas, Hugues Jacqmin, Carole Geara, Marouane Boubaya, Bernard Chatelain, Xavier TroussardAbstract:The French-Speaking Cellular Haematology Group (GFHC) recently published criteria for microscopic analysis of a blood smears when a hemogram is requested. In order to evaluate and improve these recommendations using an XN (Sysmex) analyzer, we assessed 31,836 samples categorized into two sub-groups of patients either receiving or not receiving care in the clinical hematology/oncology departments of two university hospitals. By combining the manufacturer's recommendations and the GFHC recommendations, 21.3% of samples had a positive review flag in phase 1 of our study (17,991 samples). In phase 2 (13,845 samples), increasing the immature granulocytes (IG) percentage from 5-10% as a review trigger threshold, and ignoring slides with isolated flags 'PLT HIGH' (thrombocytosis) or 'MCV LOW' (microcytosis) or 'Blast/Abn Lymph and Atypical Lymph' (blast cells/abnormal lymphocytes and atypical lymphocytes) (in the absence of abnormal cells on a previous blood smear within 72 h), enabled us to significantly reduce the number of slides reviewed from 21.3-15.0% (p < 0.0001), without loss of clinical value. This decrease occurred in both sub-groups (hematology 48.7-38.0%, non-hematology 18.3-11.7%, p < 0.0001). In conclusion, the application of the GFHC criteria adapted to XN analyzers has enabled us to optimize the hematology laboratory processes, and thus reduce the production costs and the turnaround time of hemogram results.
Mengying Zhao - One of the best experts on this subject based on the ideXlab platform.
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A Highly Parallelized PIM-Based Accelerator for Transaction-Based Blockchain in IoT Environment
IEEE Internet of Things Journal, 2020Co-Authors: Qian Wang, Zhaoyan Shen, Zhiping Jia, Mengying Zhao, Tianyu Wang, Renhai Chen, Zili ShaoAbstract:Blockchain has gained a lot of attention from both academia and industry. However, traditional standard blockchains, such as Bitcoin and Ethereum, suffer from low throughput, high computation overhead, and large transaction fee, which is not suitable for Internet of Things (IoT) transactions. Recently, transaction-based approaches, such as Tangle structure which is based on a directed acyclic graph (DAG), have emerged to solve blockchain scalability issues for IoT environment. In transaction-based blockchain, for a transaction, namely, a node, to be attached to the Tangle, it needs to verify two other transactions. However, with the Tangle expanding, this attaching process consumes huge computational resources and energy, which severely limits the performance of the transaction-based blockchain. In this article, we present Re-Tangle, a highly parallelized processing-in-memory (PIM)-based accelerator for transaction-based blockchain. Re-Tangle is composed of a random walking Module, a transaction Validation Module, and a PoW Module, to improve the Tangle system performance. These Modules transfer Tangle functions, such as fast exponentiation and modular, into ReRAM-based logic analog computation units. In the random walking Module, Re-Tangle maintains an exponentiation table to reduce its design complexity and improve its computation efficiency. In the transaction Validation Module, Re-Tangle further proposes a highly parallel modular unit to accelerate the Validation of different tags in a transaction. In the PoW Module, we decompose the Curl hash function into basic logic OR, AND, SHIFT, and XOR operations, and map these logic operations to ReRAM crossbars in parallel to accelerate the working process. The experimental results show that Re-Tangle distinguishes itself from other architectures with significant performance improvement and energy saving. The throughput of Re-Tangle is about $22.4\times $ and $2.38\times $ higher compared with CPU and GPU, respectively, and the energy consumption of Re-Tangle is $83.5\times $ and $5.77\times $ less for equal workload.
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ICCAD - Re-Tangle: A ReRAM-based Processing-in-Memory Architecture for Transaction-based Blockchain
2019 IEEE ACM International Conference on Computer-Aided Design (ICCAD), 2019Co-Authors: Qian Wang, Wang Tianyu, Zhaoyan Shen, Zhiping Jia, Mengying Zhao, Zili ShaoAbstract:Blockchain has gained a lot of attentions from both academic and industry. Transaction-based approaches such like Tangle structure, which is based on a DAG (Directed Acyclic Graph), are emerging to solve the blockchain scalability issues for IoT environment. In transaction-based blockchain, for a transaction, namely a node, to be attached to the Tangle, it needs to verify two other transactions. However, with the Tangle expanding, this attaching process consumes huge computational resources and energy, which severely limits the performance of the transaction-based blockchain. In this paper, we present Re-Tangle, a novel transaction-based blockchain acceleration architecture that explores the opportunity of performing massive parallel operations with low hardware and energy cost. Re-Tangle consists of a random walking Module and a transaction Validation Module, which transfer Tangle functions into ReRAM-based logic analog computation units. In the random walking Module, Re-Tangle maintains a exponentiation translator to reduce its design complexity and improve its computation efficiency for exponentiation. In the transaction Validation Module, Re-Tangle further proposes a highly parallel modular unit to accelerate the Validation of different tags in a transaction. The experience results show that Re-Tangle distinguishes itself from other architectures, with significant performance improvement and energy saving. The throughput of Re-Tangle is about 19.4× and 2.13× higher compared with CPU and GPU, respectively, and the energy consumption of Re-Tangle is 63.35 × and 4.92 × less.
