Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Zhewei Jiang"'
Publikováno v:
IEEE Access, Vol 8, Pp 91405-91414 (2020)
In-memory computing (IMC) is a promising approach for energy cost reduction due to data movement between memory and processor for running data-intensive deep learning applications on the computing systems. Together with Binary Neural Network (BNN), I
Externí odkaz:
https://doaj.org/article/ef9e07726523424ca1b32684a963c970
Publikováno v:
Optical Metrology and Inspection for Industrial Applications IX.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e1dba0cb8c123f65aa343db46ba0a88c
https://doi.org/10.1117/12.2651362
https://doi.org/10.1117/12.2651362
Autor:
Zhewei Jiang, Dongkwun Kim, Mingoo Seok, Ram Krishnamurthy, Suhwan Kim, Sung Justin Kim, Andres Arturo Blanco
Publikováno v:
IEEE Solid-State Circuits Letters. 4:56-59
Emerging sub-mW near-threshold-voltage system-on-chips require new power management architecture that can create multiple voltage domains with the fewest possible off-chip passives. To fulfill this need, we propose an ultra-low-power single-inductor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::025a4c8afc5793c2db95532a9e3ca96d
https://doi.org/10.1109/lssc.2021.3059818
https://doi.org/10.1109/lssc.2021.3059818
Publikováno v:
IEEE Journal of Solid-State Circuits. 55:1888-1897
This article presents C3SRAM, an in-memory-computing SRAM macro. The macro is an SRAM module with the circuits embedded in bitcells and peripherals to perform hardware acceleration for neural networks with binarized weights and activations. The macro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a06709fc82a4869efe38d9bafccd387
https://doi.org/10.1109/jssc.2020.2992886
https://doi.org/10.1109/jssc.2020.2992886
Publikováno v:
IEEE Access, Vol 8, Pp 91405-91414 (2020)
In-memory computing (IMC) is a promising approach for energy cost reduction due to data movement between memory and processor for running data-intensive deep learning applications on the computing systems. Together with Binary Neural Network (BNN), I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39b5cdcb3a5f723673ce26d2e2c61f2e
https://ieeexplore.ieee.org/document/9091590/
https://ieeexplore.ieee.org/document/9091590/
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 28:48-61
To enable essential deep learning computation on energy-constrained hardware platforms, including mobile, wearable, and Internet of Things (IoT) devices, a number of digital ASIC designs have presented customized dataflow and enhanced parallelism. Ho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9740021c025f73a7b17a2967b4c16037
https://doi.org/10.1109/tvlsi.2019.2940649
https://doi.org/10.1109/tvlsi.2019.2940649
Publikováno v:
2020 5th Asia Conference on Power and Electrical Engineering (ACPEE).
In this paper, an analysis method of sensitivity matrix for integrated electricity-natural gas system (IEGS) with gas-fired generator as energy coupling component is proposed to analyze the bidirectional impact induced by the fluctuations of power an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::87c0b058a4c44dd29887356ca41dc98d
https://doi.org/10.1109/acpee48638.2020.9136503
https://doi.org/10.1109/acpee48638.2020.9136503
Publikováno v:
ACSSC
We present Vesti, a Deep Neural Network (DNN) accelerator optimized for energy-constrained hardware platforms such as mobile, wearable, and Internet of Things (IoT) devices. Vesti integrates instances of in-memory computing (IMC) SRAM macros with an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::91489948fab3d4768c74829c1ea19a75
https://doi.org/10.1109/ieeeconf44664.2019.9048678
https://doi.org/10.1109/ieeeconf44664.2019.9048678
Publikováno v:
ISLPED
The k-nearest neighbor (kNN) is one of the most popular algorithms in machine learning owing to its simplicity, versatility, and implementation viability without any assumptions about the data. However, for large-scale data, it incurs a large amount
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13c54eb0df8e0000599a1ba2a85468a6
https://doi.org/10.1109/islped.2019.8824822
https://doi.org/10.1109/islped.2019.8824822
Publikováno v:
ACM Great Lakes Symposium on VLSI
We present an in-memory computing SRAM macro for binary neural networks. The memory macro computes XNOR-and-accumulate for binary/ternary deep convolutional neural networks on the bitline without row-by-row data access. It achieves 33X better energy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1eafe68e9c87dd9afc107fff3b7869ce
https://doi.org/10.1145/3299874.3319458
https://doi.org/10.1145/3299874.3319458