A 0.32–128 TOPS, scalable multi-chip-module-based deep neural network inference accelerator with ground-referenced signaling in 16 nm

B Zimmer, R Venkatesan, YS Shao… - IEEE Journal of Solid …, 2020 - ieeexplore.ieee.org
Custom accelerators improve the energy efficiency, area efficiency, and performance of
deep neural network (DNN) inference. This article presents a scalable DNN accelerator …
被引用次数：103 相关文章
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[PDF][PDF] A 0.32-128 TOPS, Scalable Multi-Chip-Module-based Deep Neural Network Inference Accelerator with Ground-Referenced Signaling in 16nm

B Zimmer, R Venkatesan, YS Shao - people.csail.mit.edu
Custom accelerators improve the energy efficiency, area efficiency, and performance of
deep neural network (DNN) inference. This work presents a scalable DNN accelerator …

[引用][C] A 0.32-128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm

B Zimmer, R Venkatesan, YS Shao… - IEEE Journal of …, 2020 - ui.adsabs.harvard.edu
A 0.32-128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference
Accelerator With Ground-Referenced Signaling in 16 nm - NASA/ADS Now on home page ads …

[引用][C] A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm

B Zimmer, R Venkatesan, YS Shao, J Clemons… - IEEE Journal of Solid …, 2020 - cir.nii.ac.jp
A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference
Accelerator With Ground-Referenced Signaling in 16 nm | CiNii Research CiNii 国立情報学 …